**Likelihood Functions**: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

**Bayes Theorem**: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.

**Signal-To-Noise Ratio**: The comparison of the quantity of meaningful data to the irrelevant or incorrect data.

**Algorithms**: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.

**Computer Simulation**: Computer-based representation of physical systems and phenomena such as chemical processes.

**Models, Statistical**: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

**Markov Chains**: A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.

**Models, Genetic**: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Monte Carlo Method**: In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)

**Probability**: The study of chance processes or the relative frequency characterizing a chance process.

**Data Interpretation, Statistical**: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.

**Models, Biological**: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.

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+ − Homework 2: Parsimony {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/homeworks/hw2 Parsimony.pdf}}. + − 'Bootstrapping' {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/Bootstrapping.pdf}} and 'Distance Methods' {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/Distances.pdf}} PL away: watch Bootstrapping.mov and Distances.mov br/ Bootstrapping; Distance methods: split decomposition, quartet puzzling, neighbor-joining, least squares criterion, minimum evolution criterion. + − 'Substitution models'{{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/ModelsIntro.pdf}} watch ModelsIntro.mov if PL not back yet br/ Transition probability, instantaneous rates, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model. + − Homework 3: Distances {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/homeworks/hw3 Distances.pdf}}. + − 'Maximum

The model implemented in the computer package Mendel estimates both recombination and linkage-disequilibrium parameters and conducts

... Table 1. Parameter of interest in prognostic modelling studies and ways to combine estimates after MI. Parameters. Possible methods for combining estimates of parameters after MI*. Covariate distribution. Mean Value. Rubin's rules. Standard Deviation. Rubin's rules. Correlation. Rubin's rules after Fisher's Z transformation. Model parameters. Regression coefficient. Rubin's rules. Hazard ratio. Rubin's rules after logarithmic transformation. Prognostic Index/linear predictor per patient. Rubin's rules. Model fit and performance. Testing significance of individual covariate in model. Rubin's rules using a Wald test for a single estimates Table 2 A. Testing significance of all fitted covariates in model. Rubin's rules using a Wald test for multivariate estimates Table 2 B.

... m care insurance. Advanced Search. Papers. Articles. Authors. Institutions. Rankings. Data FRED. . Advanced Search. IDEAS home Browse for material Papers. Articles. Software. Books. Chapters. Authors. Institutions. Rankings. Data FRED. Find material JEL Classification. NEP reports. Subscribe to new research. Search. Pub compilations. Reading lists. MyIDEAS. More options are now at bottom of page IDEAS is a. service hosted by the Research Division of the Federal Reserve Bank of St. Louis. To this day, RePEc has facilitated over 75 million recorded document downloads. Printed from https://ideas.repec.org/. Share:. MyIDEAS : Login to save this article or follow this journal. Multidimensional smoothing by adaptive local kernel-weighted log-

... Skip to the navigation. Skip to the content. Sitemap. Contact. Privacy. Legal. Home. Who we are. What we do. Resources. Contact us. Location:. Home. Information Matrix. The bottom line Sep 11, 2010 At it's core, the study of mortality is based on a simple ratio - the number of deaths, D, divided by the population exposed to the risk of death, E : mortality rate = D / E While this seems simple and straightforward, there are important subtleties concerning the quality of the data used. One of these is illustrated by a series of revelations about unreported deaths in Japan, where a recent audit has cast doubt on the reliability of Japanese population statistics for the elderly. Unreported deaths reduce mortality rates both by under-counting deaths and by inflating the population at risk. Time will tell if Japan's famously low mortality rates have been under-stated as a result. . Read more Tags: mortality projections, Japan. Find by key-word. Find by date. Select All September 2015 August 2015 July 2015 June...

These are often used to recover messages sent over a noisy channel, such as a binary symmetric channel. Notation Ideal observer decoding Decoding conventions. Maximum

... Thomas Buckley Thomas.Buckley at vuw.ac.nz. Wed Jun 2 18:27:50 EST 1999. Previous message: Bias and

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bayes theorem disambiguation bayes theorem disambiguation bayes theorem may refer to theorem bayes theorem a theorem which expresses how a subjective degree of belief should rationally change to account for evidence the application of the theorem bayesian theory in e discovery the application of bayes theorem in legal evidence diagnostics and e discovery where it provides a way of updating the probability of an event in the light of new information bayesian theory in marketing the application of bayes theorem in marketing where it allows for decision making and market research evaluation under uncertainty and limited data...

how would I apply Bayes theorem. - Math Help Forum. Register. Help. Register. Forums. Geometry. Pre-Calculus. Statistics. Calculus. Differential Geometry. Forum. University Math Help Forum. Advanced Statistics. how would I apply Bayes theorem. Math Help - how would I apply Bayes theorem. Subscribe to this Thread. Display Linear Mode. Switch to Hybrid Mode. Switch to Threaded Mode. May 25th 2009, 08:40 PM. crafty. Junior Member. Joined Feb 2009 Posts 45. how would I apply Bayes theorem. Machine I produces 2% defectives and machine II produces 3% defectives. If an item is not defective, what is the chance it was produced by machine I. Follow Math Help Forum on Facebook and Google+. May 25th 2009, 08:48 PM. crafty. Junior Member. Joined Feb 2009 Posts 45. what about this one matheagle. Follow Math Help Forum on Facebook and Google+. May 25th 2009, 08:49 PM. matheagle. Joined Feb 2009 Posts 2,763 Thanks 5. I will let A stand for machine one and B for machine 2. So the probability of defective is. Hence the probab...

... MATLAB Central File Exchange Answers Newsgroup Link Exchange Blogs Trendy Cody Contest MathWorks.com. File Exchange. MathWorks.com. Highlights from Mass Spectrometry Bayesian Network Analysis Tool WMBAT The William and Mary Bayesian Analysis Tool. View all files. 7 Downloads last 30 days File Size: 16.8 KB File ID: #24345 Version: 1.2 Mass Spectrometry Bayesian Network Analysis Tool by. Karl Kuschner. Karl Kuschner view profile. Updated 17 Jul 2009. Finds diagnostic features in the spectra of biologic samples by using a Bayesian Network approach Watch this File. File Information Description Starting with a group of training data and a given classification like "disease" or "non-"disease" this function builds a three level Bayesian Network from mass spectrometry data. The root node of the Bayesian network is the class variable. The first lower level contains all the features found to have high mutual information with the class variable. The second lower level are features that have high mutual information...

... Specifically, it compares the probability of finding particular evidence if the accused were guilty, versus if they were not guilty. An example would be the probability of finding a person's hair at the scene, if guilty, versus if just passing through the scene. Suppose, that the proposition to be proven is that defendant was the source of a hair found at the crime scene. Before learning that the hair was a genetic match for the defendant’s hair, the factfinder believes that the odds are 2 to 1 that the defendant was the source of the hair. If she used Bayes’ theorem, she could multiply those prior odds by a “

Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images PDF Download Available. Article Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images. Page 1 Bayesian Classification of Multiple Sclerosis Lesions in Longitudinal MRI Using Subtraction Images. Our method was evaluated on a a scan-rescan data set con- sisting of 3 MS patients and b a multicenter clinical data set consisting of 212 scans from 89 RRMS relapsing-remitting MS patients. Our method was evaluated on a a scan-rescan data set consisting of 3 MS patients and b a multicenter clinical data set consisting of 212 scans from 89 RRMS patients with 2-4 longitudinal scans each. The MC labels for the reference timepoint were used as a prior for the Bayesian classifier BC. Means and standard deviations of lesion volume at reference, new lesion voxels, resolved lesion voxels and change in lesion volume over the 3 scan-rescan patients are shown in Table 1. Scan-Rescan prec...

5 Choosing the prior distribution 104 5.1 Introduction 104 5.2 The sequential use of Bayes theorem 104 5.3 Conjugate prior distributions 106 5.4 Noninformative prior distributions 113 5.5 Informative prior distributions 121 5.6 Prior distributions for regression models 129 5.7 Modeling priors 134 5.8 Other regression models 136 5.9 Closing remarks 136. 9 Hierarchical models 227 9.1 Introduction 227 9.2 The Poisson-gamma hierarchical model 228 9.3 Full versus empirical Bayesian approach 238 9.4 Gaussian hierarchical models 240 9.5 Mixed models 244 9.6 Propriety of the posterior 260 9.7 Assessing and accelerating convergence 261 9.8 Comparison of Bayesian and frequentist hierarchical models 263 9.9 Closing remarks 265. 11 Variable selection 319 11.1 Introduction 319 11.2 Classical variable selection 320 11.3 Bayesian variable selection: Concepts and questions 325 11.4 Introduction to Bayesian variable selection 326 11.5 Variable selection based on Zellner s g-prior 333 11.6 Variable selection based on Reversibl...

Further DNA segmentation analysis using approximate Bayesian computation. The University of Newcastle's Digital Repository. The University of Newcastle's Digital Repository. List Of Titles Further DNA segmentation analysis using approximate Bayesian computation. Title Further DNA segmentation analysis using approximate Bayesian computation Creator Allingham, David ; King, Robert A. The University of Newcastle. Here, ABC is applied to a model of DNA sequence segmentation containing boundary locations and a first-order hidden Markov model HMM of the nucleotide transition probabilities. 2007 used a Kullback-Leibler-based summary statistic and distance measure for the case where segment boundary locations were known, allowing estimation of the nucleotide transition probabilities. In this paper, an alternative summary statistic is proposed which enables the simultaneuous estimation of boundary location and transition probability posterior distributions. The first-order HMM models pairs of nucleotides, and so oligo...

... Naive Bayes vs. Logistic Regression. There's often confusion as to the nature of the differences between Logistic Regression and Naive Bayes Classifier. One way to look at it is that Logistic Regression and NBC consider the same hypothesis space, but use different loss

Rewind Data Augmentation to EM. Healthy Algorithms. Healthy Algorithms. What is data augmentation. January 2, 2013 8:00 am Rewind Data Augmentation to EM The original paper on Data Augmentation DA got me thinking it was time to have a careful look at the original paper on EM. These are both highly cited papers, and Google scholar says the DA paper has been cited 2538 times a lot. and the EM paper has been cited 31328 times is that possible. Comments Off on Rewind Data Augmentation to EM Filed under statistics Tagged as EM. What is data augmentation. Data for Injury paper. academic culture aco ai4hm algorithms baby animals Bayesian books code conference contest costs data sharing data viz disclosure limitation disease modeling dismod ebola response EM free/open source funding gaussian processes gbd global health grad students health inequality health metrics health records idv IDV4GH ihme infoviz ipython iraq journal club machine learning malaria matching algorithms matchings MCMC media mortality mpld3 my rese...

... ach for genomically enhanced prediction of breeding values. User guide. Legal guide. Reference : Bayesian integration of external information into the single step approach for genomi... Title : Bayesian integration of external information into the single step approach for genomically enhanced prediction of breeding values. Author, co-author : Vandenplas, Jérémie. Event organizer : ADSA/ASAS. Keywords : Bayesian ; external information ; single step genomic procedure. However, current developments of genomic selection will bias evaluations because only records related to selected animals will be available. The single step genomic evaluation ssGBLUP could reduce pre-selection bias by the combination of genomic, pedigree and phenotypic information which are internal for the ssGBLUP. But, in opposition to multi-step methods, external information, i.e. information from outside ssGBLUP, like EBV and associated reliabilities from Multiple Across Country Evaluation which represent a priori known phenotypic informa...

Does God Exist. AppBrain Android Market. AppBrain. Browse Apps. All apps. Does God Exist. Does God Exist. by Michael Borland. Contact. Does God Exist. This application helps you explore an age-old question, Does God Exist. You'll be asked to indicate how consistent each observation is with the existence and non-existence of God. In the end, the odds that God exists are computed. After you've done a calculation, you can share the results via email, Google+, etc. Facebook doesn't work, but that's Facebook's fault not mine. Some suggestions for use: 1. Questions you can try to answer using the app: 1. Is an indifferent or evil God more probable than an all-powerful, loving God. 2 Is a God with limited power more probable than an all-powerful God. Please use the Email developer link to send your suggestions. Mathematical note: the app uses Bayes' theorem to perform the calculations, with a prior probability of 50%. Note that the reason that each observation has two questions is that we need these two responses to...

... The Infinite Monkey Theorem Petite Sirah is Vegan Friendly. http://theinfinitemonkeytheorem.com/contact/. Company email September 2013 : "We do not use any animal products with our wine, we use plant based enzymes and we use cellulose paper for filtering. Our fruit comes to us as grapes and sometimes as juice, in both cases there is no animal products introduced before it gets to our winery. The Infinite Monkey Theorem The Blind Watchmaker Red. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infinite Monkey Theorem Malbec - Magnum. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infinite Monkey Theorem Cabernet Franc - Magnum. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infinite Monkey Theorem Black Alley White. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infin...

... S Bayesian models for sparse regression analysis of high dimensional data , paper , rejoinder. BMC Bioinformatics. journal page Bottolo L; Richardson S. Blangiardo M; Richardson S. BMC Bioinformatics. 2008 "Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods" Statistical Applications in Genetics and Molecular Biology: Vol. journal page 2007 Blangiardo M., Richardson S. Bochkina N., Richardson S. 2007 Tail posterior probability for inference in pairwise and multiclass gene expression data , Biometrics, journal page , with supplementary material Lau, J. J 2007 Bayesian Model Based Clustering Procedures Journal of Computational and Graphical Statistics vol: 16 , Pages: 525 - 558, journal page Lewin, A., Bochkina, N. and Richardson, S 2007 Fully Bayesian mixture model for differential gene expression: simulations and model checks. Statistical Applications in Genetics and Molecular Biology Vol. journal page BGmix software. Lewin, A., Richards...

Divergence Theorem Applications. Divergence Theorem Applications A selection of articles related to divergence theorem applications. Original articles from our library related to the Divergence Theorem Applications. Mind >> World Mind Herbs of Folklore and Modern Medicine: White Willow, Aloe Vera, and Garlic Many of the medicinal herbs we use today have been studied by modern science for application in today’s medical field. Body Mysteries >> Homeopathy Hypnosis: A Selected Bibliography Index of bibliography sections Selected Periodicals 14 entries Edited Overviews of General Theories of Hypnosis 5 entries Specific Topics Related to Research into Hypnosis. Remedies >> Remedies A Divergence Theorem Applications is described in multiple online sources, as addition to our editors' articles, see section below for printable documents, Divergence Theorem Applications books and related discussion. Suggested Pdf Resources Divergence Theorem Examples. Divergence Theorem Examples. Gauss' divergence theorem relates ...

In Bayesian statistics, a 'hyperparameter' is a parameter of a prior distribution ; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter 'p' of a Bernoulli distribution, then:. One may take a single value for a given hyperparameter, or one can iterate and take a probability distribution on the hyperparameter itself, called a hyperprior. One often uses a prior which comes from a parametric family of probability distributions – this is done partly for explicitness so one can write down a distribution, and choose the form by varying the hyperparameter, rather than trying to produce an arbitrary function, and partly so that one can 'vary' the hyperparameter, particularly in the method of ' conjugate prior s,' or for 'sensitivity analysis.'. When using a conjugate prior, the posterior distribution will be from the same family, but will have different hyperparameters, wh...

... resources. Gene Volume Control, Graphic Science Scientific American, Jun 2015. Nature Methods Points of View visualization column. Nature Methods Points of Significance statistics column. `pi` Day 2013 Art Posters. 2013 `pi` day. 2014 `pi` day. 2015 `pi` day. If you're not into details, you may opt to party on July 22nd, which is `pi` approximation day `\pi` 22/7. news + thoughts Association, correlation and causation Thu 01-10-2015 Correlation implies association, but not causation. Nature Methods Points of Significance column: Association, correlation and causation. ...more about the Points of Significance column Bayesian networks Thu 01-10-2015 For making probabilistic inferences, a graph is worth a thousand words. This month we continue with the theme of Bayesian statistics and look at Bayesian networks, which combine network analysis with Bayesian statistics. 2015 Points of Significance: Bayesian Statistics Nature Methods 12 :277-278. 2015 Points of Significance: Bayes' Theorem Nature Methods 12 :27...

... te external information into genetic evaluations. O pen R epository and Bi bliography BICTEL/e. PoPuPS. Other OA projects at the ULg. Submitter guide. User guide. Legal guide. Tools box. FAQ. Glossary. Help. University of Liège. Library Network. Login. You are here:. ORBi. Detailled reference. Reference : Comparison and improvements of different Bayesian procedures to integrate external in... Document type : Scientific journals : Article. Discipline s : Life sciences : Genetics & genetic processes Life sciences : Animal production & animal husbandry. To cite this reference: http://hdl.handle.net/2268/103440. Title : Comparison and improvements of different Bayesian procedures to integrate external information into genetic evaluations. Language : English. Author, co-author : Vandenplas, Jérémie. Gengler, Nicolas. Publication date : Mar-2012. Journal title : Journal of Dairy Science. Publisher : American Dairy Science Association. Volume : 95. Issue/season : 3. Pages : 1513-1526. Peer reviewed : Yes verifie...

... Tutorial given at the useR. 2014 conference in Los Angeles Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark. Goals Introduce participants to using R for working with graphical models in particular graphical log-linear models for discrete data contingency tables and to probability propagation in Bayesian networks. Outline There will be a running example about building a probabilistic expert system for a medical diagnosis from real-world data. Probability propagation with Bayesian networks BNs and their implementation in the gRain gRaphical independence networks package. A look under the hood of BNs to understand mechanisms of probability propagation. Dependency graphs and conditional independence restrictions. Log-linear models, graphical models, decompsable models and their implementation in the gRim gRaphical independence models package. Model selection with gRim Converting a decompsable graphical model to a Bayesian network. Prerequisites Attendees are assumed to have a...

This post is about the beauty of a simple mathematical theorem and its applications in logic, machine learning and signal processing: Bayes’ theorem. During the way we will develop a simple recommender engine for Firefox add-ons based on data from Telemetry and reconstruct a signal from a noisy channel. tl;dr: If you couldn’t care less about mathematical beauty and probabilities, just have a look at the recommender system. Where P D|H is the

... risk factor. - UCL Discovery. UCL Discovery. UCL home » Library Services » Electronic resources » UCL Discovery Enter your search terms. Advanced search. UCL Theses. For everyone Open Access. About UCL Discovery. UCL e-theses guidelines. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor. Abstract Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used ...

Hales Jewett Theorem. realmagick.com The shrine of knowledge. Hales Jewett Theorem Density version of Hales - Jewett theorem. Hales Jewett Theorem is described in multiple online sources, as addition to our editors' articles, see section below for printable documents, Hales Jewett Theorem books and related discussion. Suggested Pdf Resources The Hales - Jewett Theorem. Importance of HJT. • The Hales - Jewett theorem is presently one of the most useful techniques in Ramsey theory. www.ti.inf.ethz.ch. A new proof of the density Hales - Jewett theorem. RAMSEY THEORY: VAN DER WAERDEN'S THEOREM AND THE. A density version of the Hales - Jewett theorem - Stanford University The well known theorem of Hales and Jewett., Theorem 1. math.stanford.edu. The Hales - Jewett theorem. This chapter presents a deep combinatorial theorem by Hales and Jewett, stated Hales - Jewett theorem 7.2, from which the classical version follows easily. Suggested News Resources Heute in den Feuilletons Wahrscheinlichkeit eines verhängnis...

... uo-Liang Tian, Kai Wang Ng - Kenward - 2010 - International Statistical Review - Wiley Online Library. Skip to Main Content Please log in or register to access this feature. Log in / Register. Log In. E-Mail Address. Password. Forgotten Password. Remember Me. Register. Institutional Login. Home. Statistics. Applied Probability Statistics. International Statistical Review. Vol 78 Issue 3. Abstract. JOURNAL TOOLS Get New Content Alerts. Get RSS feed. Save to My Profile. Get Sample Copy. Recommend to Your Librarian. JOURNAL MENU Journal Home FIND ISSUES Current Issue. All Issues FIND ARTICLES Early View. Most Accessed. Most Cited. GET ACCESS Subscribe / Renew. FOR CONTRIBUTORS OnlineOpen. Author Guidelines. Submit an Article. ABOUT THIS JOURNAL Society Information. News. Overview. Editorial Board. Permissions. Advertise. Contact. SPECIAL FEATURES Interview Papers. Discussion Papers. To Our Authors: Newsletter. Short Book Reviews Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computati...

mcmc. bayesianbiologist. bayesianbiologist Corey Chivers on P A|B ∝P B|A P A Menu Skip to content Home. About. Tag Archives: mcmc October 18, 2012. Introduction to Bayesian Methods guest lecture By Corey Chivers. Posted in Probability, Rstats, Teaching. Tagged bayesian, ecology, mcmc, methods, teaching. 7 Comments This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides. March 22, 2012. Montreal R Workshop: Introduction to Bayesian Methods By Corey Chivers. Posted in Probability, Rstats, Teaching. Tagged bayesian, mcmc, models, R user group, rstats, workshop. Leave a comment Monday, March 26, 2012 14h-16h, Stewart Biology N4/17. Corey Chivers, Department of Biology McGill University. This is a meetup of the Montreal R User Group. Be sure to join the group and RSVP. More information about the workshop here. T...

The Density Cluster Tree: A Guest Post November 23, 2012 – 7:33 pm. This post is about the notion of consistency, the CD estimator, and its analysis. 4 Analysis. Posted in Uncategorized. WHAT IS BAYESIAN/FREQUENTIST INFERENCE. Nate Silver’s book, various comments on my blog, comments on other blogs, Sharon McGrayne’s book, etc have made it clear to me that there is still a lot of confusion about what Bayesian inference is and what Frequentist inference is. Frequentist inference and Bayesian Inference are defined by their goals, not their methods. Saying That Confidence Intervals Do Not Represent Degrees of Belief Is Not a Criticism of Frequentist Inference. Saying That Posterior Intervals Do Not Have Frequency Coverage Properties Is Not a Criticism of Bayesian Inference. And conversely, it is possible to do Bayesian inference without using Bayes’ theorem as Michael Goldstein, for example, has shown. As I will discuss in that review, Nate argues forcefully that Bayesian analysis is superior to Frequentist anal...

... 'Bayesian approaches to brain function' investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. It is frequently assumed that the nervous system maintains internal probabilistic model s that are updated by neural processing of sensory information using methods approximating those of Bayesian probability. N Rao Editor 2007, Bayesian Brain: Probabilistic Approaches to Neural Coding, The MIT Press; 1 edition Jan 1 2007 Knill David,Pouget Alexandre 2004, The Bayesian brain: the role of uncertainty in neural coding and computation,TRENDS in Neurosciences Vol.27 No.12 December 2004. Origins Psychophysics Neural coding Electrophysiology Predictive coding Free energy See also References External links. 2008 Was Helmholtz a Bayesian?" 'Perception' 39, 642–50 The basic idea is that the nervous system needs to organize sensory data into an accurate internal model of the outside world. During the 1990s researc...

doi: 10.1186/2046-1682-4-10. For three measurements, one from each of the three spectral channels, the

"To Bayes or Not to Bayes. A Comparison of Two Classes of Models of Inf" by David V. Budescu and Hsiu-Ting Yu. Search. Browse Collections. My Account. Digital Commons Network™. DigitalResearch@Fordham. My Account. FAQ. Psychology. Faculty. Psychology Faculty Publications. To Bayes or Not to Bayes. A Comparison of Two Classes of Models of Information Aggregation. Authors. David V. Budescu, Fordham University University of Illinois at Urbana-Champaign at time of publication Hsiu-Ting Yu, University of Illinois at Urbana-Champaign. APA Citation: Budescu, D. V & Yu, H. To Bayes or not to Bayes. A comparison of two classes of models of information aggregation. Disciplines. Psychology. We model the aggregation process used by individual decision makers DMs who obtain probabilistic information from multiple, possibly nonindependent, sources. We distinguish between two qualitatively different aggregation approaches: compromising, by averaging the advisors’ opinions, and combining the forecasts according to a naïve im...

. Structure Learning with Nonparametric Decomposable Models - Microsoft Research. . Our research Connections Careers About us. Microsoft Translator. All Downloads Events Groups News People Projects Publications Videos. Structure Learning with Nonparametric Decomposable Models Anton Schwaighofer, Mathäus Dejori, Volker Tresp, and Martin Stetter 2007. Abstract We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be handled in a unified framework. Also, consistency of the underlying probabilistic model is guaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data. Details Publication type Inproceedings. Published in Artificial N...

Designing Optimal Sequential Experiments for a Bayesian Classifier. Designing Optimal Sequential Experiments for a Bayesian Classifier. The Community for Technology Leaders. CSDL. Institutions and Libraries. Resources. RSS Feeds. Terms of Use. Peer Review. Subscribe. CSDL Home. IEEE Transactions on Pattern Analysis & Machine Intelligence. Issue No.03 - March. Subscribe. Designing Optimal Sequential Experiments for a Bayesian Classifier. Issue No.03 - March 1999 vol.21. Robert Davis. Armand Prieditis. DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.754585. ABSTRACT. p b Abstract /b As computing power has grown, the trend in experimental design has been from techniques requiring little computation towards techniques providing better, more general results at the cost of additional computation. This paper continues this trend presenting three new methods for designing experiments. A summary of previous work in experimental design is provided and used to show how these new methods generalize previous c...

cs p bayes theorem many of the methods used for dealing with uncertainty in expert systems are based on bayes theorem notation p a probability of event a p a b probability of events a and b occurring together p a b conditional probability of event a given that event b has occurred if a and b are independent then p a b p a expert systems usually deal with events that are not independent e g a disease and its symptoms are not independent bayes theorem p a b p a b p b p b a p a therefore p a b p b a p a p b contents nbsp nbsp nbsp page nbsp nbsp nbsp prev nbsp nbsp nbsp next nbsp nbsp nbsp page nbsp nbsp nbsp index nbsp nbsp nbsp...

... e-edition. classifieds. cars. homes. FEATURED. official s foundation dissolved in Lenawee court ... Former Adrian man gets $120K grant for science research ... Meetings established for those impacted by dementia ... official s foundation dissolved in Lenawee court ... Former Adrian man gets $120K grant for science research ... Meetings established for those impacted by dementia ... Don Bayes, Sr. Don Bayes, Sr., age 62, of Adrian passed away peacefully, Monday, Dec. 16, 2013, at his home, surrounded by his loving family. Comment. The Daily Telegram - Adrian, MI. Posted Dec. Posted Dec. 17, 2013 at 10:17 AM ADRIAN. Don Bayes, Sr., age 62, of Adrian passed away peacefully, Monday, Dec. 16, 2013, at his home, surrounded by his loving family. 11, 1951, in Adrian, to Richard B. and Phyllis Fowle Bayes, Sr. In addition to his wife of 35 years, Renee, Don is survived by his three sons, Don Amy Bayes, Jr. of Ypsilanti, Doug Bayes and Coty Bayes of Adrian; two daughters, Karissa Kubacki of Adrian and Brieana Core...

sosgssd the university of western ontario southern ontario statistics graduate students seminar days the university of western ontario homepage accommodations registration poster advertisement contact summer school outline schedule seminar days outline submit an abstract list of presentations sponsors day summer school date may the summer workshop is a three day intensive workshop on spatial statistics for non gaussian data taught by patrick brown from cancer care ontario and virgilio gomez rubio from the university of castilla la mancha spain the material will cover advanced topics including the generalized linear geostatistical model spatial models for disease mapping bayesian inference using integrated nested laplace approximation markov random field approximations to geostatistical processes and methods for spatio temporal data the instruction will be evenly divided between lectures and supervised computer labs the material will be suitable for students with the equivalent of one year of graduate statisti...

There is nothing big data about Nate's methods. That's big data. That is big data. The fact is, he didn't need big data to do this. Look at it this way, if a meteorologist says there a 90% chance of rain where you live and it doesn't rain, the forecast wasn't necessarily wrong, because 10% of the time it shouldn't rain - otherwise the odds would be something other than a 90% chance of rain. In a frequentist interpretation, saying that an outcome of an event has a probability X% of occuring, you're saying that if you were to run an infinite series of repetitions of the event, then on average, the outcome would occur in X out of every 100 events. What it says is: for any specific event, it will have one outcome. But given the current state of information available to me , I can have a certain amount of certainty about whether or not the event will occur. It just means that given the current state of my knowledge, I expect a particular outcome, and the information I know gives me that degree of certainty. Bayesi...

Updating is really just a form of learning, but Bayes’ theorem gives us a way to structure that learning that turns out to be very powerful. A big part of my professional life involves using statistical models to forecast rare political events, and I am deeply frustrated by frequent encounters with people who dismiss statistical forecasts out of hand see here and here for previous posts on the subject .Â It’s probably unrealistic of me to think so, but I am hopeful that recognition of the intuitive nature and power of Bayesian updating might make it easier for skeptics to make use of my statistical forecasts and others like them. I’m a firm believer in the forecasting power of statistical models, so I usually treat a statistical forecast as my initial belief or prior, in Bayesian jargon and then only revise that forecast as new information arrives. When the statistical forecast diverges from your prior belief, Bayes’ theorem offers a structured but simple way to arrive at a new estimate.Â Experience shows tha...

Background Objectives Computer simulation modeling Gaussian process prior. Design of computer experiments Problems with massive sample sizes. 'Bias correction': Use physical data to correct for bias in the simulation. Computer simulation modeling. Modeling of computer experiments typically uses a Bayesian framework. The basic idea of this framework is to model the computer simulation as an unknown function of a set of inputs. It is natural to see the simulation as a deterministic function that maps these 'inputs' into a collection of 'outputs'. For some simulations, such as climate models, evaluation of the output for a single set of inputs can require millions of computer hours. Gaussian process prior. The typical model for a computer code output is a Gaussian process. Owing to the Bayesian framework, we fix our belief that the function f follows a Gaussian process, f \sim \operatorname{GP} m \cdot,C \cdot,\cdot, where m is the mean function and C is the covariance function. Popular mean

www nutritionj com table table change in prior probabilities of cafestol not affecting serum cholesterol to posterior probabilities using data of the present study and bayesian analysis prior probability prior odds yes no posterior odds posterior probability very strong x bayes factor strong x bayes factor equivocal x bayes factor weak x bayes factor very weak x bayes factor a priori probabilities were converted to a priori odds and multiplied by the minimum bayes factor the obtained a postiori odds were converted to a postiori probabilities bayes factor e to the power z where z is the z score corresponding to the p value for obtaining an effect of mmol l under the null hypothesis p value z score the minimum bayes factor boekschoten et al nutrition journal doi...

... s. Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics. The Community for Technology Leaders. CSDL. Institutions and Libraries. Resources. RSS Feeds. Newsletter. Terms of Use. Peer Review. Login. Subscribe. CSDL Home. IEEE Transactions on Knowledge & Data Engineering. Issue No.04 - July/August. Subscribe. Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics. Issue No.04 - July/August 2000 vol.12. Daniel Nikovski. DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.868904. ABSTRACT. p b Abstract /b The paper discusses several knowledge engineering techniques for the construction of Bayesian networks for medical diagnostics when the available numerical probabilistic information is incomplete or partially correct. This situation occurs often when epidemiological studies publish only indirect statistics and when significant unmodeled conditional dependence exists in the problem domain. While nothing can ...

... c skaaning f jensen u kjaerulff and a madsen when developing real world applications of bayesian networks one of the largest obstacles is the highly time consuming process of gathering probabilistic information this paper presents an efficient technique applied for gathering probabilistic information for the large sacso system for printing system diagnosis the technique allows the domain experts to provide their knowledge in an intuitive and efficient manner the knowledge is formulated in terms of

Nature, 432, 1014-1017, doi:10.1038/nature03174. J Geophys., 109, D10303, doi:10.1029/2003JD004173. J Geophys., 109, D04313, doi:10.1029/2003JD003527. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. J Geophys., 109, D10304, doi:10.1029/2003JD004174. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 3. J Geophys., 109, D10305, doi:10.1029/2003JD004175. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics: A remote sensing application. Cairns, and A. Marshak, 2004: Automated algorithm for remote sensing of atmospheric aerosols and trace gases using MFRSR measurements. SPIE, vol. 5571, 238, doi:10.1117/12.565306. Cairns, A.A. Geophys. Lett., 31, L04118, doi:10.1029/2003GL019105. Cairns, A.A. Sci., 61, 1024-1039, doi:10.1175/1520-0469 2004 061 1024:SPOAOT 2.0.CO;2. Miller, C. Rosenzweig, W. Sci., 1023, 125-141, doi:10.1196/annals.1319.005. Forest Meteorol. I...

... Professor of Statistical Science, Duke University. Home. Bio. Papers. Software. DSS. Duke. Research Interests Model uncertainty and choice in prediction and variable selection problems for linear, generalized linear models and multivariate models. Bayesian Model Averaging. Prior distributions for model selection and model averaging. Wavelets and adaptive non-parametric function estimation. Spatial statistics. Experimental design for nonlinear models. Applications in proteomics, bioinformatics, astro-statistics, air pollution and health effects, and environmental sciences. Recent Papers Iversen, E.S., Lipton, G., Clyde, M., Monteiro, A. 2014 Functional Annotation Signatures of Disease Susceptibility Loci Improve SNP Association Analysis. BMC Genomics 15:398 http://dx.doi.org/10.1186/1471-2164-15-398 Clyde, M. A and Iversen, E.S. 2013 Bayesian Model Averaging in the M-Open Framework. In ``Bayesian Theory and Applications' edited by P. Damien, P. Dellaportas, N.G. Polson and D.A. Stephens. Oxford University...

... infobox software name winbugs logo image bugs logo gif screenshot caption collapsible author developer the bugs project released discontinued yes latest release version latest release date winbugs is statistical software for bayesian analysis using markov chain monte carlo mcmc methods it is based on the bugs b ayesian inference u sing g ibbs s ampling project started in it runs under microsoft windows though it can also be run on linux or mac using wine it was developed by the bugs project a team of uk researchers at the mrc biostatistics unit cambridge and imperial college school of medicine london the last version of winbugs was version released in august development is now focused on openbugs an open source version of the package winbugs remains available as a stable version for routine use but is no longer being developed references external links category statistical software category monte carlo software category windows only free software...

Other Article Types. Article-Level Metrics. Article. New York data were used to estimate numerators for ICU and death, and two sources of data—medically attended cases in Milwaukee or self-reported influenza-like illness ILI in New York—were used to estimate ratios of symptomatic cases to hospitalizations. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% 95% credible interval 0.026%–0.096%, sCIR of 0.239% 0.134%–0.458%, and sCHR of 1.44% 0.83%–2.64%. By using data on medically attended and hospitalized cases of pH1N1 infection in Milwaukee and information from New York City on hospitalizations, intensive care use, and deaths, the researchers estimate that the proportion of US cases with symptoms that died the sCFR during summer 2009 was 0.048%. When the researchers used a different approach to estimate the total number of symptomatic cases—based on New Yorkers' self-reported incidence of influenza-like-illness from a telepho...

complex analysis - Extension of Liouville's Theorem. - Mathematics Stack Exchange. Mathematics Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. Mathematics Questions. Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Extension of Liouville's Theorem. Liouville's Theorem states that if a function is bounded and holomorphic on the complex plane i.e. bounded and entire, then it is a constant function. Can we use Liouville's Theorem to somehow conclude that $f$ is a constant function. Conan Wong Dec 5 '12 at 21:33. user27126 Dec 5 '12 at 22:13. A constant modulus on the closure of a domain gives that the function is constant. add a comment. I don't see how you could use Liouville's theorem to prove that, but it does follow from Cauchy-Riemann's equations. 2 = u 2 + v 2$ to show that both $u$ and $v$ must be constant on $D$. After that it...

HELENE MASSAM, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 A conjugate prior for discrete hierarchical loglinear models In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the loglinear parameters or the cell probabilities parameters is a major challenge. In this talk we define a flexible family of conjugate priors for the wide class of discrete hierarchical loglinear models which includes the class of graphical models. The talk is concerned with a procedure for testing whether this function belongs to a given parametric family. WEI NING, Bowling Green State University, Department of Mathematics and Statistics A Generalized Lambda Distribution GLD Change Point Model For the Detection of DNA Copy Number Variations in Array CGH Data In this talk, we study the detection of the multiple change points of parameters of generalized lambda distributions GLD. The derivation of SIC indicates that SIC serves as an asymptotic approximation to a transformatio...

... Mathematics Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. Mathematics Questions. Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Issue while applying Master Theorem. I've read about the master theorem for solving recurrences in Introduction to Algorithms, but have a problem probably, due to misunderstanding while applying it in some cases. For example, having recurrence $T n = 5 T \frac{n}{3} + \Theta n 2 \log n $ and trying to apply this theorem I have: $a=5; b=3; f n =\Theta n 2 \log n $. But as I understand, $f n = \Theta n 2 \log n $ doesn't imply regularity of $f n $ and the master theorem is impossible to apply in this case. It would help if you told us what the master theorem says. Marek Feb 13 '13 at 14:07. Marek Feb 13 '13 at 14:31. Because $g n $ is regular, we can apply the master theorem to it and it gives us the same r...

pr.probability - The $\sigma 0$ condition in the Central Limit Theorem - MathOverflow. more stack exchange communities. Stack Exchange. The $\sigma 0$ condition in the Central Limit Theorem. In the version of central limit theorem for strictly stationary but weakly dependent for instance $\alpha$-mixing with fast decaying mixing coefficient random variables $X_1, X_2, \cdots$, the theorem in this Wikipedia page states see also Billingsley 1995 Theorem 27.4 : Theorem. Suppose that $X_1, X_2, \cdots$ is stationary and $\alpha$-mixing with $\alpha_n = O n {−5} $ and that $\mathbb{E} X_n = 0$ and $\mathbb{E} X_n {12} \infty$. Denote $S_n = X_1 + \cdots + X_n$, then the limit $\sigma 2 = \lim_{n\to \infty} \mathbb{E} S_n 2/n$ exists, and if $\sigma \ne 0$ then $S_n/ \sigma \sqrt{n} $ converges in distribution to the standard Gaussian distribution $\mathcal{N} 0, 1 $. Then $X_1, X_2 \cdots$ is stationary and $\alpha$-mixing with $\alpha_n = 0$ for all $n \ge 2$. $$ In the above example though the right hand side be...

... Login. Search Browse Simple Search. Advanced Search. Browse by Subject. Browse by Year. Browse by Conferences/Volumes. Browse Open Access Journals. Latest Additions. About the Archive. Archive Policy. History. Help. FAQ. Journal Eprint Policies. Contact Us. Bayesian Networks and the Problem of Unreliable Instruments. Bovens, Luc and Hartmann, Stephan 2000 Bayesian Networks and the Problem of Unreliable Instruments. Preview. PDF Download 2214Kb. Preview. Abstract We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider i repeated measurements of a single testable consequence of the hypothesis, ii measurements of multiple testable consequences of the hypothesis, iii theoretical support for the reliability of the instrument, and iv calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and s...

combinatorics - A consequence of Wilson's Theorem - Mathematics Stack Exchange. Stack Exchange. Help Center Detailed answers to any questions you might have. Mathematics Questions. A consequence of Wilson's Theorem. By Wilson's Theorem we know that $$ p-1. \equiv -1 \mod p.$$ A consequence of this is apparently $$ p- k+1 !k. \equiv -1 {k+1} \mod p$$ where $0 \leq k \leq p-1$. Then $ p-i \equiv -i \mod p.$ I'm interested in proceeding in the above way, but I'm not sure how to. 4 Answers 4 active oldest votes. Hint $\ $ An equivalent way to state Wilson's theorem is that any complete system of representatives of nonzero remainders mod $\,n\,$ has product $\equiv -1.\,$ In particular this is true for any sequence of $\,p\,$ consecutive integers, after removing its $\rm\color{#c00}{multiple}$ of $\,p.\,$ Your special case is the sequence $\, -k,\,-k\!+\!1,\ldots,-1,\require{cancel}\cancel{\color{#c00}0,} 1,2,\ldots, p\!-\!k\!-\!1,\,$ with product $ -1 k k!\, p\!-\!k\!-\!1 !\equiv -1.\ $ QED Remark $\ $ Th...

... chat blog. MathOverflow. MathOverflow Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. MathOverflow Questions. Sign up. MathOverflow is a question and answer site for professional mathematicians. Shannon-McMillan-Breiman Theorem. up vote 4 down vote favorite 1. Does anyone know of an easy proof of Shannon-McMillan-Brieman Theorem. ergodic-theory share. improve this question. asked Nov 30 '11 at 14:30. Igor Rivin Nov 30 '11 at 14:36. I agree with Igor Rivin's comment: please try to phrase your question in a more useful and less subjective way. add a comment. 2 Answers 2 active oldest votes. Weiss, " The Shannon-McMillan-Breiman theorem for a class of amenable groups ", Israel J. improve this answer. add a comment. up vote 1 down vote. improve this answer. Igor Rivin 49.6k. Igor Rivin Nov 30 '11 at 19:37. Thank you very much Igor. add a comment. discard By posting your answer, you agree to the privacy policy and te...

... 'Cross-species transmission CST ' is the phenomenon of transfer of viral infection from one species, usually a similar species, to another. Often seen in emerging viruses where one species transfers to another which in turn transfers to humans. Examples include HIV-AIDS, SARS, Ebola, Swine flu, rabies, and Bird flu. Faria NR, Suchard MA, Rambaut A, Streicker DG, Lemey P. Simultaneously reconstructing viral cross-species transmission history and identifying the underlying constraints. Philos Trans R Soc Lond B Biol Sci. 2013 Feb 4;368 1614. The exact mechanism that facilitates the transfer is unknown, however, it is believed that viruses with a rapid mutation rate are able to overcome host-specific immunological defenses. This can occur between species that have high contact rates. It can also occur between species with low contact rates but usually through an intermediary species. Host Phylogeny Constrains Cross-Species Emergence and Establishments of Rabies Virus in bats. Similarity between species, for...

probability - How do I find these bounds using Chebychev's inequality and the Central Limit Theorem. - Mathematics Stack Exchange. Stack Exchange. Help Center Detailed answers to any questions you might have. Mathematics Questions. How do I find these bounds using Chebychev's inequality and the Central Limit Theorem. Find a lower bound using Chebychev's inequality. Approximate the value using the central limit theorem. Do you know what Chebychev's inequality says. I think I figured out finding the lower bound using Chebychev's inequality. The central limit theorem deals with zn converging with standard normal distribution. In what way can you think of your Gamma-distributed random variable as a sum of iid random variables. The central limit theorem says that the average of iid random variables $y i$ having mean $\mu$ and variance $\sigma 2$ converges to a normal distribution with mean $\mu$ and variance $\sigma 2/n$. You know that we can express the gamma-distribution random variable $X$ as $$...

... 'Bayesian Operational Modal Analysis' BAYOMA adopts a Bayesian system identification approach for Operational Modal Analysis OMA. Operational Modal Analysis OMA aims at identifying the modal properties natural frequencies, damping ratio s, mode shape s, etc. The input excitations to the structure are not measured but are assumed to be ' ambient ' 'broadband random'. In a Bayesian context, the set of modal parameters are viewed as uncertain parameters or random variables whose probability distribution is updated from the prior distribution before data to the posterior distribution after data. The peak s of the posterior distribution represents the most probable value s 'MPV' suggested by the data, while the spread of the distribution around the MPV reflects the remaining uncertainty of the parameters. Pros and Cons Methods Notes See also References. In the absence of input loading information, the identified modal properties from OMA often have significantly larger uncertainty or variability than their co...

Complete CV Research interests Courses taught EPIB-682 Introduction to Bayesian Analysis in the Health Sciences EPIB-683 Intermediate Bayesian Analysis for the Health Sciences Previous courses taught EPIB-607 Principles of Inferential Statistics in Medicine EPIB-613 Introduction to Statistical Software EPIB-621 Data Analysis in the Health Sciences EPIB-651 Bayesian Analysis in Medicine EPIB-668 Introduction to Bayesian Analysis in the Health Sciences EPIB-669 Intermediate Bayesian Analysis for the Health Sciences EPIB-675 Bayesian Analysis in the Health Sciences Publications Methodological publications Bayesian Sample Size Change-point methods and applications Diagnostic testing Meta analysis Other topics Medical publications Allergy and immunology Asthma Cardiology Gastroenterology Osteoporosis Quality of Life Rheumatology Other Book Bayesian software Bayesian Sample Size Change-point methods and applications Diagnostic testing Diagnostic testing in Genetics Links. Courses taught EPIB-682 Introduction to Bay...

October, 2013. R news and tutorials contributed by 573 R bloggers Home About RSS add your blog. Here you will find daily news and tutorials about R, contributed by over 573 bloggers. Data Scientist – Machine Learning in booking.com @ Amsterdam. Data Scientist – Analytics in booking.com @ Amsterdam. Hadley Wickham s Ask Me Anything on Reddit. Learn R interactively with our new Introduction to R tutorial. Monthly Archives: October 2013 Detecting an Unfair Die with Bayes’ Theorem October 30, 2013 By Eric Cai - The Chemical Statistician Introduction I saw an interesting problem that requires Bayes Theorem and some simple R programming while reading a bioinformatics textbook. The Problem The following question is. I have currently been doing this the Continue reading. What Hadley Wickham uses October 30, 2013 By David Smith You know Hadley Wickham as the inventor of the ggplot2 visualization phenomenon, the creator of time-saving R packages like plyr and lubridate, and the Chief Scientist at RStudio. Binomial conf...

In statistics, 'deviance' is a quality of fit statistic for a model that is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares of residuals in ordinary least squares to cases where model-fitting is achieved by maximum

... The Non-English Olympic Team. The BBC article on the Team GB Olympic kit entertains your humble correspondent. People I respect have been writing that The Powers That Be are hostile to England and the English for some time. Posted by. Dead Dog Bounce. Email This. BlogThis. Share to Twitter. Share to Facebook. Share to Pinterest. That Rod Liddle article. A disgrace, that's what it is. I've read the Rod Liddle article, and it's a disgrace. So, what is a disgrace in my view. Posted by. Dead Dog Bounce. Email This. BlogThis. Share to Twitter. Share to Facebook. Share to Pinterest. Bayes Theorem Banned. Quick post: judge bans Bayes Theorem from evidence according to the Guardian. The classic teaching example is of a test for a rare disease. If the disease affects 1/100,000 and the false positive rate is 1/10,000, and the test is 100% reliable otherwise, the probability is that a positive outcome is a false positive is 90%+. Posted by. Dead Dog Bounce. Email This. BlogThis. Share to Twitter. Share to Facebook....

co.combinatorics - Helm's improvement to Beck-Fiala theorem - MathOverflow. MathOverflow. MathOverflow Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. MathOverflow Questions. Sign up. MathOverflow is a question and answer site for professional mathematicians. Helm's improvement to Beck-Fiala theorem. up vote 3 down vote favorite 1. Beck-Fiala theorem states that if X is a finite set and H is any family of subsets of X, in which every vertex occurs in at most d sets of H, then there is a a function f:X->{ 1} such for every set S in H we have |sum x in S f x. There were two papers that improve the bound of 2d-2. The later improvement is due to Helm to 2d-4. co.combinatorics share. improve this question. Boris Bukh 4,431. add a comment. 2 Answers 2 active oldest votes. up vote 2 down vote. The author Martin Helm seems nowadays to be a faculty member in financial engineering at Baruch College, see this http://www.bar...

... Project People. Log In. New Account. Home. My Page. Projects. Software Map Tag cloud. Project Tree. Project List. Project tree. Now limiting view to projects in the following categories: Topic :: Bayesian Statistics :: Specific Model Fitting. Programming Language :: R. Programming Language :: Tcl. Operating System :: OS Independent. Natural Language :: English. Development Status :: 4 - Beta. Topic Bayesian Statistics 52 projects Bioinformatics 155 projects Biostatistics Medical Statistics 69 projects Chemoinformatics 17 projects Cluster Analysis 43 projects Computational Physics 12 projects Connectivity 21 projects Database 28 projects Datasets 29 projects Design of Experiments Analysis of Experimental Data 17 projects Econometrics 67 projects Education 27 projects Environmetrics 43 projects Finance 69 projects Genetics 67 projects Graphical Models 9 projects Graphical User Interface 21 projects Graphics 78 projects High Performance Computing 31 projects Machine Learning 73 projects Marketing Business A...

... default search action combined dblp search. author search. venue search. publication search. Publications: search using CompleteSearch. Chunping Wang. export bibliography BibTeX. RIS. RDF. XML. dblp keys. dblp key: homepages/54/2715. ask others Google. Google Scholar. MS Academic Search. see FAQ. see FAQ. What is the meaning of the colors in the publication lists. view electronic edition via DOI. export record BibTeX. RIS. RDF. XML. dblp key: conf/wocc/WangZW13. ask others Google. Google Scholar. MS Academic Search. PubZone Weihong Wang,. Shusheng Zheng, Chunping Wang : Reprogramming for target nodes in WSNs. view electronic edition via DOI. export record BibTeX. RIS. RDF. XML. dblp key: conf/cso/ChenDW12. ask others Google. Google Scholar. MS Academic Search. PubZone Lin Chen,. Dayong Deng, Chunping Wang : F-Parallel Reducts in the Information View. view electronic edition via DOI. export record BibTeX. RIS. RDF. XML. dblp key: conf/fuzzIEEE/MeiSW12. ask others Google. Google Scholar. MS Academic Search...

... Accept Lyrics. The Beast Lyrics. Lyrics Depot is your source of lyrics to The Beast by Accept. Please check back for more Accept lyrics. The Beast Lyrics Artist: Accept Album: Death Row. My mind is razor sharp And I'm wild-cat-mean I'd like to shred your face A danger to you When I feel the urge Deep down inside I lose my human touch I'm like a killing machine I want to resist - but can't hold it back The beast is unleashed - it's got to attack Again and again the force is too strong It's breaching the chains I've got the beast inside Never ever trust in me I've got the beast inside Craving a victim - it's telling me to kill I look through evil eyes Prowling in the streets In the dead of night I'm a man on the hunt I know my soul is under siege Can't somebody stop me now 'cause I'm losing control I've got the beast inside Don't you trust in me I know it must have been the beast inside It's driving me to kill It's gotta be the beast - the beast - the beast The beast - the beast - the beast - deep down ins...

ini abstracts scbw exact bayesian inference and model selection for some infection models the ini has a new website this is a legacy webpage please visit the new site to ensure you are seeing up to date information an isaac newton institute workshop stochastic computation for the analysis of ecological and epidemiological data exact bayesian inference and model selection for some infection models authors damian clancy university of liverpool philip d o neill university of nottingham abstract while much progress in the analysis of infectious disease data depends upon mcmc methodology the simpler and more exact method of rejection sampling can sometimes be very useful using examples of influenza data from a population divided into households this talk will illustrate the use of rejection sampling in model fitting use of an initial sample to improve the efficiency of the algorithm selection between competing models of differing dimensionality navigation workshop timetable stochastic computation for the analysis ...

shockley ramo theorem shockley ramo theorem the shockley ramo theorem allows one to easily calculate the instantaneous electric current induced by a charge moving in the vicinity of an electrode it is based on the concept that current induced in the electrode is due to the instantaneous change of electrostatic flux line s which end on the electrode not the amount of charge received by the electrode per second the theorem appeared in william shockley s paper currents to conductors induced by a moving point charge and a year later in simon ramo s paper entitled currents induced by electron motion the shockley ramo theorem states that the instantaneous current i induced on a given electrode due to the motion of a charge is given by i e v q v where q is the charge of the particle v is its instantaneous velocity and e v is the component of the electric field in the direction of v at the charge s instantaneous position under the following conditions charge removed given electrode raised to unit potential and all ot...

church rosser theorem church rosser theorem right px in mathematics and theoretical computer science the church rosser theorem states that when applying reduction rules to term s in the lambda calculus the ordering in which the reductions are chosen does not make a difference to the eventual result more precisely if there are two distinct reductions or sequences of reductions that can be applied to the same term then there exists a term that is reachable from both results by applying possibly empty sequences of additional reductions the theorem was proved in by alonzo church and j barkley rosser after whom it is named the theorem is symbolized by the diagram at right if term a can be reduced to both b and c then there must be a further term d possibly equal to either b or c to which both b and c can be reduced viewing the lambda calculus as an abstract rewriting system the church rosser theorem states that the reduction rules of the lambda calculus are confluent as a consequence of the theorem a term in the l...

Tag Archive. You are currently browsing the tag archive for the ‘Gowers uniformity norms’ tag. Deducing the inverse theorem for the multidimensional Gowers norms from the one-dimensional version. 24 July, 2015 in expository, math.CO. Tags: Ben Green, Freiman isomorphism, Gowers uniformity norms, Nikos Frantzikinakis. by Terence Tao. 1 comment. This week I have been at a Banff workshop “ Combinatorics meets Ergodic theory “, focused on the combinatorics surrounding SzemerÃ©di’s theorem and the Gowers uniformity norms on one hand, and the ergodic theory surrounding Furstenberg’s multiple recurrence theorem and the Host-Kra structure theory on the other. This was quite a fruitful workshop, and directly inspired the various posts this week on this blog. Incidentally, BIRS being as efficient as it is, videos for this week’s talks are already online. As mentioned in the previous two posts, Ben Green, Tamar Ziegler, and myself proved the following inverse theorem for the Gowers norms:. Theorem 1 Inverse theorem for ...

Is there a simple way to prove Bertrand's postulate from the prime number theorem. - Mathematics Stack Exchange. Stack Exchange. Help Center Detailed answers to any questions you might have. Mathematics Questions. Is there a simple way to prove Bertrand's postulate from the prime number theorem. Is there a simple way to prove Bertrand's postulate from the prime number theorem. Is the prime number theorem really "stronger" than Bertrand's postulate, in the sense that assuming the former can simplify a proof of the latter. EDIT: I am specifically referring to this version of PNT: $\pi x \sim \frac{x}{\log x}$. prime-numbers analytic-number-theory share. To prove Chebyshev's theorem, one can start by finding the estimate $$ \frac{2 {2n}}{2n+1} \binom{2n}{n} 2 {2n}, \tag{1} $$ then using it to conclude that $\vartheta x /x 4\log 2$ and hence $$ \limsup_{x \to \infty} \frac{\pi x }{x/\log x} = \limsup_{x \to \infty} \frac{\vartheta x }{x} \leq 4 \log 2, $$ and $\psi x /x x-2 \log 2/x - \log x+1 /x$ and hen...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

... The INI has a new website. DAE. Seminars. Pratola, M. DAE Seminar. Bayesian Calibration of Computer Model Ensembles Pratola, M Los Alamos National Laboratory Friday 09 September 2011, 11:30-12:00 Seminar Room 1, Newton Institute. Abstract Using field observations to calibrate complex mathematical models of a physical process allows one to obtain statistical estimates of model parameters and construct predictions of the observed process that ideally incorporate all sources of uncertainty. Many of the methods in the literature use response surface approaches, and have demonstrated success in many applications. However there are notable limitations, such as when one has a small ensemble of model runs where the model outputs are high dimensional. In such instances, arriving at a response surface model that reasonably describes the process can be dicult, and computational issues may also render the approach impractical. In this talk we present an approach that has numerous beneifts compared to some popular m...

... in statistics bayesian vector autoregression bvar uses bayesian methods to estimate a vector autoregression var in that respect the difference with standard var models lies in the fact that the model parameters are treated as random variable s and prior probabilities are assigned to them vector autoregressions are flexible statistical models that typically include many free parameters given the limited length of standard macroeconomic datasets bayesian methods have become an increasingly popular way of dealing with this problem of over parameterization the general idea is to use informative priors to shrink the unrestricted model towards a parsimonious naïve benchmark thereby reducing parameter uncertainty and improving forecast accuracy see for a survey a typical example is the shrinkage prior proposed by robert litterman and subsequently developed by other researchers at university of minnesota which is known in the bvar literature as the minnesota prior the informativeness of the prior can be set by t...

Laplace s Rule of Succession. Membership. Member Discount Programs. Portrait Gallery. Guidelines for Convergence Authors. MAA Library Recommendations. Calendar of Events. MAA Distinguished Lecture Series. Past MAA Distinguished Lectures. MAA MathFest. Mathematical Sessions. Other Mathematical Sessions. Undergraduate Student Activities. SIGMAA Activities. Joint Mathematics Meetings. MAA Policies. MAA Section Meetings. You are here Home » Programs » Faculty and Departments » Course Communities » Laplace s Rule of Succession Laplace s Rule of Succession. Related Resources: Buffon s Needle Problem - Expository introduction Bernoulli Trials: Introduction Conditional Probability and Independent Events Conditional Probability Bayes Theorem Bayesian odds Expectation - Introduction Bayes Ratio: Dramatic Taxicab Example Dependent and Independent Events Principle of Proportionality Independent Events and Independent Experiments Conditional Recurrence Construction of Pascal s Triangle. Browse Classroom Capsules and Notes...

favard s theorem favard s theorem in mathematics favard s theorem also called the shohat favard theorem states that a sequence of polynomials satisfying a suitable term recurrence relation is a sequence of orthogonal polynomials the theorem was introduced in the theory of orthogonal polynomials by and though essentially the same theorem was used by stieltjes in the theory of continued fraction s many years before favard s paper and was rediscovered several times by other authors before favard s work statement suppose that y y is a sequence of polynomials where y n has degree n if this is a sequence of orthogonal polynomials for some positive weight function then it satisfies a term recurrence relation favard s theorem is roughly a converse of this and states that if these polynomials satisfy a term recurrence relation of the form y n x c n y n d n y n for some numbers c n and d n then the polynomials y n form an orthogonal sequence for some linear function λ with λ in other words λ y m y n if m n the linear f...

tverberg s theorem tverberg s theorem image tverberg heptagon svg in discrete geometry tverberg s theorem first stated by is the result that sufficiently many points in d dimensional euclidean space can be partitioned into subset s with intersecting convex hull s specifically for any set of d r points there exists a point x not necessarily one of the given points and a partition of the given points into r subsets such that x belongs to the convex hull of all of the subsets the partition resulting from this theorem is known as a tverberg partition examples for r tverberg s theorem states that any d points may be partitioned into two subsets with intersecting convex hulls this special case is known as radon s theorem in this case for points in general position there is a unique partition the case r and d states that any seven points in the plane may be partitioned into three subsets with intersecting convex hulls the illustration shows an example in which the seven points are the vertices of a regular heptagon ...

Post navigation. ← Dark matter benchmarks: All over the map. Introduction to Bayesian lecture: Accompanying handouts and demos →. October 18, 2012 Introduction to Bayesian Methods guest lecture. By Corey Chivers ¶. Posted in Probability, Rstats, Teaching. ¶. Tagged bayesian, ecology, mcmc, methods, teaching. ¶. 7 Comments. This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides. . Share this:. Twitter Facebook Reddit Google. Like this:. Like Loading... Related....

Methods for the Exploration of Posterior Distributions and

**Likelihood****Functions**(Springer ... Methods for the Exploration of Posterior Distributions and**Likelihood****Functions**(Springer ... Methods for the Exploration of Posterior Distributions and**Likelihood****Functions**(Springer ... Methods for the Exploration of Posterior Distributions and**Likelihood****Functions**(Springer ......http://fr.adeli-center.com/ebooks/tools-for-statistical-inference-methods-for-the-exploration-of-posterior-distributions-and

Steps Toward Artificial Intelligence - - -Marvin Minsky... can handle a maximum-

**likelihood**type of analysis of the output of the property**functions**... "success function" E which is a reasonably smooth function of the coordinates. Here we can ... The function C simply counts the number of points remaining in the picture. ... Multiple simultaneous optimizers search for a (local) maximum value of some function E (x ......http://web.media.mit.edu/~minsky/papers/steps.html

Consulting-Specifying EngineerPFDavg is an assessment of the

**likelihood**that the safety function will not work as ... The safety function of the ESD valve remains available throughout the PST, continuing to ... Functioning much like the black box on an airplane, the device captures and transmits ... A SIS is composed of one or more safety instrumented**functions**(SIFs), each of which ......http://csemag.com/single-article/hart-technology-partial-stroke-valve-testing-a-powerful-duo-for-improving-plant-safety/67c223feae9483bedff6a0e39c7d201c.html

Mini MBA Certificate | Pepperdine University | Graziadio Business SchoolWith data being ubiquitous, data science is no longer a function carried out by a select ... Through the application of these models and technology, you can improve the

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Systematic review of controlled trials of interventions to promote smoke alarms...Effects on the prevalence of functioning smoke alarms from clinical counselling were less ... counselling and educational interventions had only a modest effect on the

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Marginal **likelihood**inference for a model for item responses and response times...... no differential item functioning), the shape of the response

**functions**, and three ... Marginal**likelihood**inference for a model for item responses and response times. Authors ... Marginal maximum-**likelihood**procedures for parameter estimation and testing the fit of a ... Extending the Floor and the Ceiling for Assessment of Physical Function, Arthritis & ......http://onlinelibrary.wiley.com/doi/10.1348/000711009X481360/abstract?globalMessage=0

System and Method for Improving a User's Demographic Predictions by Using Both...Therefore, the long-

**likelihood**of the objective function can be:. where L3 is the third ......http://priorart.ip.com/IPCOM/000246610

Evaluating Test Strategies for Colorectal Cancer Screening: A Decision Analysis...The

**likelihood**of adenoma growth and progression to CRC is allowed to vary by location in ... face a monthly cancer-specific mortality rate that is a function of the stage at ... While annual (often age-specific) probabilities define the**likelihood**of transitioning ... takes place for each simulated individual is drawn from a cumulative probability function ......http://pubmedcentralcanada.ca/pmcc/articles/PMC2731975/?lang=en-ca

Elevated resting heart rate, physical fitness and all-cause mortality: a 16...Cardiac Function. This is an open-access article distributed under the terms of the ... using the maximum

**likelihood**ratio method and a backward stepwise elimination procedure. ... Cardiac Function. Introduction. Elevated resting heart rate has been shown to be ... the use of Cox proportional hazards were met by inspection of the log minus log function ......http://heart.bmj.com/content/99/12/882.full

THE ESTIMATION OF A SYSTEM PULSE TRANSFER FUNCTION IN THE PRESENCE OF NOISE,... applied to obtain generalized least-squares estimates which are also maximum

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[R] Poisson Maximum **Likelihood**Estimationpoisson maximum

**likelihood**estimation poisson maximum**likelihood**estimation paul sweeting mail at paulsweeting co uk fri jan cet previous message for loop faster than vectorized code next message poisson maximum**likelihood**estimation messages sorted by hi i am trying to carry out some maximum**likelihood**estimation and i m not making much headway and i m hoping that someone will be able to point me in the right direction i am modelling mortality statistics one way to do this is to model the mortality rate or more accurately log of the mortality rate log m as say a constant plus a proportion of age plus time so r lm formula log m age time summary r however an alternative approach is to use try and estimate the number of deaths from the poisson mean mortality rate and the number of people with the poisson mean being defined in terms of age and time and a constant conceptually i can see how this should work in terms of linking the poisson probabilities together at each age and optimising the coefficients on age a...https://stat.ethz.ch/pipermail/r-help/2008-January/152567.html

Empirical **likelihood**confidence intervals for complex sampling designs - ePrints Soton... . Advanced Search University Home. ePrints Soton Policies Latest Additions Download Statistics Browse by Year Browse by Subject Browse by School. Login. RSS 1.0. RSS 2.0. Atom. Empirical

**likelihood**confidence intervals for complex sampling designs . Berger, Y.G. and De La Riva Torres, O. 2016 Empirical**likelihood**confidence intervals for complex sampling designs. Journal of the Royal Statistical Society: Series B Statistical Methodology, 1-23. doi:10.1111/rssb.12115. Download. PDF - Accepted Manuscript Restricted to System admin until 5 April 2016. Download 470Kb. Request a copy. Description/Abstract We define an empirical**likelihood**approach which gives consistent design-based confidence intervals which can be calculated without the need of variance estimates, design effects, resampling, joint inclusion probabilities and linearization, even when the point estimator is not linear. It can be used to construct confidence intervals for a large class of sampling designs and estimators which are...http://eprints.soton.ac.uk/337688/

Estimating from Cross-sectional Categorical Data Subject to Misclassification and Double Sampling:... Moment-based, Maximum

**Likelihood**and Quasi-**Likelihood**Approaches - ePrints Soton. ePrints Soton Policies Latest Additions Download Statistics Browse by Year Browse by Subject Browse by School. Estimating from Cross-sectional Categorical Data Subject to Misclassification and Double Sampling: Moment-based, Maximum**Likelihood**and Quasi-**Likelihood**Approaches. Estimating from Cross-sectional Categorical Data Subject to Misclassification and Double Sampling: Moment-based, Maximum**Likelihood**and Quasi-**Likelihood**Approaches. Southampton, UK, Southampton Statistical Sciences Research Institute, 34 pp. Description/Abstract We discuss the analysis of cross-sectional categorical data in the presence of misclassification and double sampling. We then show that the misclassification model can be alternatively formulated as a missing data problem using the misclassification probabilities. We suggest that the formulation of the misclassification model as a missing data problem using the misclassification probabilities, a...http://eprints.soton.ac.uk/8176/

Difference between revisions of "Phylogenetics: Syllabus" - EEBedia+ − Homework 2: Parsimony {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/homeworks/hw2 Parsimony.pdf}}. + − 'Bootstrapping' {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/Bootstrapping.pdf}} and 'Distance Methods' {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/Distances.pdf}} PL away: watch Bootstrapping.mov and Distances.mov br/ Bootstrapping; Distance methods: split decomposition, quartet puzzling, neighbor-joining, least squares criterion, minimum evolution criterion. + − 'Substitution models'{{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/ModelsIntro.pdf}} watch ModelsIntro.mov if PL not back yet br/ Transition probability, instantaneous rates, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model. + − Homework 3: Distances {{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/homeworks/hw3 Distances.pdf}}. + − 'Maximum

**likelihood**'{{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/6**Likelihood**.pdf}} br/ Poisson processes;**Likelihood**...http://hydrodictyon.eeb.uconn.edu/eebedia/index.php?title=Phylogenetics:_Syllabus&diff=27745&oldid=17764

Association Testing in a Linked Region Using Large Pedigrees - ResearchGateThe model implemented in the computer package Mendel estimates both recombination and linkage-disequilibrium parameters and conducts

**likelihood**-ratio tests for 1 linkage alone, 2 linkage and association simultaneously, and 3 association in the presence of linkage. In addition to the esti- mation of the recombination fraction v, separating the trait locus and the marker, we propose the estimation of the conditional frequency of the disease allele, given each marker allele. In the parametric framework of maximum-**likelihood**estimation, one can test null hy- potheses, such as no linkage and no association, of marker allele pi quan- q dFi 1 2 v p and q dFi p q for all i, by a**likelihood**-ratio test, where q is the frequency of the disease allele. Table 2 LOD Scores andTests with Lumped Alleles 2 x SNP s a LOD SCORE FOR TEST 2 xdf STATISTIC 2 x P VALUE Linkage Association and Linkage 1 1.34 1.90 2 1 x p 2.58.108 2 1.973.46 2 1 x p 6.86 .010 3 1.82 2.90 2 1 x p 4.97 .026 1, 21.94 3.73 2 2 x p 8.24 .016 2, 31.02 1.91 ...http://researchgate.net/publication/8073265_Association_Testing_in_a_Linked_Region_Using_Large_Pedigrees

www.biomedcentral.com - Table... Table 1. Parameter of interest in prognostic modelling studies and ways to combine estimates after MI. Parameters. Possible methods for combining estimates of parameters after MI*. Covariate distribution. Mean Value. Rubin's rules. Standard Deviation. Rubin's rules. Correlation. Rubin's rules after Fisher's Z transformation. Model parameters. Regression coefficient. Rubin's rules. Hazard ratio. Rubin's rules after logarithmic transformation. Prognostic Index/linear predictor per patient. Rubin's rules. Model fit and performance. Testing significance of individual covariate in model. Rubin's rules using a Wald test for a single estimates Table 2 A. Testing significance of all fitted covariates in model. Rubin's rules using a Wald test for multivariate estimates Table 2 B.

**Likelihood**ratio χ 2 test statistic. Rules for combining**likelihood**ratio statistics if parametric model Table 2 D or χ 2 statistics if Cox model Table 2 C. Proportion of variance explained e.g. R 2 statistics. Robust methods. Discrimina...http://biomedcentral.com/1471-2288/9/57/table/T1

Multidimensional smoothing by adaptive local kernel-weighted log-**likelihood**: Application to long-ter... m care insurance. Advanced Search. Papers. Articles. Authors. Institutions. Rankings. Data FRED. . Advanced Search. IDEAS home Browse for material Papers. Articles. Software. Books. Chapters. Authors. Institutions. Rankings. Data FRED. Find material JEL Classification. NEP reports. Subscribe to new research. Search. Pub compilations. Reading lists. MyIDEAS. More options are now at bottom of page IDEAS is a. service hosted by the Research Division of the Federal Reserve Bank of St. Louis. To this day, RePEc has facilitated over 75 million recorded document downloads. Printed from https://ideas.repec.org/. Share:. MyIDEAS : Login to save this article or follow this journal. Multidimensional smoothing by adaptive local kernel-weighted log-

**likelihood**: Application to long-term care insurance. Contents: Author info. Abstract. Bibliographic info. Download info. Related research. References. Citations. Lists. Statistics. Corrections. Author Info. Tomas, Julien Planchet, Frédéric. Registered author s : Frédéric Pl...https://ideas.repec.org/a/eee/insuma/v52y2013i3p573-589.html

Information Matrix... Skip to the navigation. Skip to the content. Sitemap. Contact. Privacy. Legal. Home. Who we are. What we do. Resources. Contact us. Location:. Home. Information Matrix. The bottom line Sep 11, 2010 At it's core, the study of mortality is based on a simple ratio - the number of deaths, D, divided by the population exposed to the risk of death, E : mortality rate = D / E While this seems simple and straightforward, there are important subtleties concerning the quality of the data used. One of these is illustrated by a series of revelations about unreported deaths in Japan, where a recent audit has cast doubt on the reliability of Japanese population statistics for the elderly. Unreported deaths reduce mortality rates both by under-counting deaths and by inflating the population at risk. Time will tell if Japan's famously low mortality rates have been under-stated as a result. . Read more Tags: mortality projections, Japan. Find by key-word. Find by date. Select All September 2015 August 2015 July 2015 June...

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Decoding methodsThese are often used to recover messages sent over a noisy channel, such as a binary symmetric channel. Notation Ideal observer decoding Decoding conventions. Maximum

**likelihood**decoding Minimum distance decoding Syndrome decoding Partial response maximum**likelihood**Viterbi decoder See also Sources References. One may be given the message x \in \mathbb{F} 2 n, then 'ideal observer decoding' generates the codeword y \in C. : \mathbb{P} y \mbox{ sent} \mid x \mbox{ received}. Maximum**likelihood**decoding. Given a received codeword x \in \mathbb{F} 2 n ' maximum**likelihood**decoding' picks a codeword y \in C that maximize s. : \mathbb{P} x \mbox{ received} \mid y \mbox{ sent},. : \begin{align} \mathbb{P} x \mbox{ received} \mid y \mbox{ sent} & {} = \frac{ \mathbb{P} x \mbox{ received}, y \mbox{ sent} }{\mathbb{P} y \mbox{ sent} } \\ & {} = \mathbb{P} y \mbox{ sent} \mid x \mbox{ received} \cdot \frac{\mathbb{P} x \mbox{ received} }{\mathbb{P} y \mbox{ sent} }. Upon fixing \mathbb{P} x \mbox{ received}, x is restr...https://en.wikipedia.org/wiki/Decoding_methods

Bias and **likelihood**... Thomas Buckley Thomas.Buckley at vuw.ac.nz. Wed Jun 2 18:27:50 EST 1999. Previous message: Bias and

**likelihood**Next message: LAMARC: update of migrate version 0.7. Messages sorted by:. If you are concerned with a lack of stationarity in the data, you could try analysing the data under the LogDet substitution model with an appropriate proportion of invariable sites removed. The LogDet model is not constrained by the assumption of reversibility, so should be resistant to any shifts in the substitution process over the tree. Although it will still be susceptable to changes in the distribution of sites free to vary. Hope this helps, Thomas Jean-Fran ois Martin wrote: In butterfly mtDNA, the composition bias is extreme toward A-T 80 to 90% depending on gene and codon position. It seems also unlikely that every kind of substitution has equal probability to occur. Furthermore a selection against substitutions providing G and C, which has been demonstrated in Dloop of mammalians A-T rich, is not correctly repres...http://bio.net/bionet/mm/mol-evol/1999-June/006683.html

LLR... may refer to llr mm a type of mortar used by the french army lender of last resort banking term leukaemia lymphoma research log

**likelihood**ratio lucas lehmer riesel an algorithm to find the primality of a number of the form k n lunar laser ranging...https://en.wikipedia.org/wiki/LLR

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Bayes theorem (disambiguation)bayes theorem disambiguation bayes theorem disambiguation bayes theorem may refer to theorem bayes theorem a theorem which expresses how a subjective degree of belief should rationally change to account for evidence the application of the theorem bayesian theory in e discovery the application of bayes theorem in legal evidence diagnostics and e discovery where it provides a way of updating the probability of an event in the light of new information bayesian theory in marketing the application of bayes theorem in marketing where it allows for decision making and market research evaluation under uncertainty and limited data...

https://en.wikipedia.org/wiki/Bayes_theorem_(disambiguation)

how would I apply Bayes theorem? - Math Help Forumhow would I apply Bayes theorem. - Math Help Forum. Register. Help. Register. Forums. Geometry. Pre-Calculus. Statistics. Calculus. Differential Geometry. Forum. University Math Help Forum. Advanced Statistics. how would I apply Bayes theorem. Math Help - how would I apply Bayes theorem. Subscribe to this Thread. Display Linear Mode. Switch to Hybrid Mode. Switch to Threaded Mode. May 25th 2009, 08:40 PM. crafty. Junior Member. Joined Feb 2009 Posts 45. how would I apply Bayes theorem. Machine I produces 2% defectives and machine II produces 3% defectives. If an item is not defective, what is the chance it was produced by machine I. Follow Math Help Forum on Facebook and Google+. May 25th 2009, 08:48 PM. crafty. Junior Member. Joined Feb 2009 Posts 45. what about this one matheagle. Follow Math Help Forum on Facebook and Google+. May 25th 2009, 08:49 PM. matheagle. Joined Feb 2009 Posts 2,763 Thanks 5. I will let A stand for machine one and B for machine 2. So the probability of defective is. Hence the probab...

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Mass Spectrometry Bayesian Network Analysis Tool - File Exchange - MATLAB Central... MATLAB Central File Exchange Answers Newsgroup Link Exchange Blogs Trendy Cody Contest MathWorks.com. File Exchange. MathWorks.com. Highlights from Mass Spectrometry Bayesian Network Analysis Tool WMBAT The William and Mary Bayesian Analysis Tool. View all files. 7 Downloads last 30 days File Size: 16.8 KB File ID: #24345 Version: 1.2 Mass Spectrometry Bayesian Network Analysis Tool by. Karl Kuschner. Karl Kuschner view profile. Updated 17 Jul 2009. Finds diagnostic features in the spectra of biologic samples by using a Bayesian Network approach Watch this File. File Information Description Starting with a group of training data and a given classification like "disease" or "non-"disease" this function builds a three level Bayesian Network from mass spectrometry data. The root node of the Bayesian network is the class variable. The first lower level contains all the features found to have high mutual information with the class variable. The second lower level are features that have high mutual information...

http://mathworks.com/matlabcentral/fileexchange/24345-mass-spectrometry-bayesian-network-analysis-tool

Evidence under Bayes theorem... Specifically, it compares the probability of finding particular evidence if the accused were guilty, versus if they were not guilty. An example would be the probability of finding a person's hair at the scene, if guilty, versus if just passing through the scene. Suppose, that the proposition to be proven is that defendant was the source of a hair found at the crime scene. Before learning that the hair was a genetic match for the defendant’s hair, the factfinder believes that the odds are 2 to 1 that the defendant was the source of the hair. If she used Bayes’ theorem, she could multiply those prior odds by a “

**likelihood**ratio” in order to update her odds after learning that the hair matched the defendant’s hair. The**likelihood**ratio is a statistic derived by comparing the odds that the evidence expert testimony of a match would be found if the defendant was the source with the odds that it would be found if defendant was not the source. It teaches that the relevance of evidence that a proposition is true...https://en.wikipedia.org/wiki/Evidence_under_Bayes_theorem

Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images (Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images PDF Download Available. Article Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images. Page 1 Bayesian Classification of Multiple Sclerosis Lesions in Longitudinal MRI Using Subtraction Images. Our method was evaluated on a a scan-rescan data set con- sisting of 3 MS patients and b a multicenter clinical data set consisting of 212 scans from 89 RRMS relapsing-remitting MS patients. Our method was evaluated on a a scan-rescan data set consisting of 3 MS patients and b a multicenter clinical data set consisting of 212 scans from 89 RRMS patients with 2-4 longitudinal scans each. The MC labels for the reference timepoint were used as a prior for the Bayesian classifier BC. Means and standard deviations of lesion volume at reference, new lesion voxels, resolved lesion voxels and change in lesion volume over the 3 scan-rescan patients are shown in Table 1. Scan-Rescan prec...

http://researchgate.net/publication/46818720_Bayesian_classification_of_multiple_sclerosis_lesions_in_longitudinal_MRI_using_subtraction_images

Wiley: Bayesian Biostatistics - Emmanuel Lesaffre, Andrew B. Lawson5 Choosing the prior distribution 104 5.1 Introduction 104 5.2 The sequential use of Bayes theorem 104 5.3 Conjugate prior distributions 106 5.4 Noninformative prior distributions 113 5.5 Informative prior distributions 121 5.6 Prior distributions for regression models 129 5.7 Modeling priors 134 5.8 Other regression models 136 5.9 Closing remarks 136. 9 Hierarchical models 227 9.1 Introduction 227 9.2 The Poisson-gamma hierarchical model 228 9.3 Full versus empirical Bayesian approach 238 9.4 Gaussian hierarchical models 240 9.5 Mixed models 244 9.6 Propriety of the posterior 260 9.7 Assessing and accelerating convergence 261 9.8 Comparison of Bayesian and frequentist hierarchical models 263 9.9 Closing remarks 265. 11 Variable selection 319 11.1 Introduction 319 11.2 Classical variable selection 320 11.3 Bayesian variable selection: Concepts and questions 325 11.4 Introduction to Bayesian variable selection 326 11.5 Variable selection based on Zellner s g-prior 333 11.6 Variable selection based on Reversibl...

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Further DNA segmentation analysis using approximate Bayesian computation | NOVA. The University of NFurther DNA segmentation analysis using approximate Bayesian computation. The University of Newcastle's Digital Repository. The University of Newcastle's Digital Repository. List Of Titles Further DNA segmentation analysis using approximate Bayesian computation. Title Further DNA segmentation analysis using approximate Bayesian computation Creator Allingham, David ; King, Robert A. The University of Newcastle. Here, ABC is applied to a model of DNA sequence segmentation containing boundary locations and a first-order hidden Markov model HMM of the nucleotide transition probabilities. 2007 used a Kullback-Leibler-based summary statistic and distance measure for the case where segment boundary locations were known, allowing estimation of the nucleotide transition probabilities. In this paper, an alternative summary statistic is proposed which enables the simultaneuous estimation of boundary location and transition probability posterior distributions. The first-order HMM models pairs of nucleotides, and so oligo...

http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6124?letter=W&sort=metadata.peerreviewed\

Machine Learning, etc: Naive Bayes vs. Logistic Regression... Naive Bayes vs. Logistic Regression. There's often confusion as to the nature of the differences between Logistic Regression and Naive Bayes Classifier. One way to look at it is that Logistic Regression and NBC consider the same hypothesis space, but use different loss

**functions**, which leads to different models for some datasets. To see that both logistic regression and naive bayes classifier consider the same hypothesis space we can rewrite Naive Bayes density as follows restricting attention to binary domain :. You can see that the conditional term is equivalent to logistic regression, so every possible conditional density that can be modelled by logistic regression can be modelled by Naive Bayes by setting \phi's aribrarily. So both logistic regression and Naive Bayes have the same hypothesis space, but optimize different objective**functions**. If empirical density is realizable by our model, then this coupling doesn't matter -- both conditional and marginal terms can achieve their respective maxima so ...http://yaroslavvb.blogspot.com/2006/04/naive-bayes-vs-logistic-regression.html?showComment=1259707359206

Rewind Data Augmentation to EM | Healthy AlgorithmsRewind Data Augmentation to EM. Healthy Algorithms. Healthy Algorithms. What is data augmentation. January 2, 2013 8:00 am Rewind Data Augmentation to EM The original paper on Data Augmentation DA got me thinking it was time to have a careful look at the original paper on EM. These are both highly cited papers, and Google scholar says the DA paper has been cited 2538 times a lot. and the EM paper has been cited 31328 times is that possible. Comments Off on Rewind Data Augmentation to EM Filed under statistics Tagged as EM. What is data augmentation. Data for Injury paper. academic culture aco ai4hm algorithms baby animals Bayesian books code conference contest costs data sharing data viz disclosure limitation disease modeling dismod ebola response EM free/open source funding gaussian processes gbd global health grad students health inequality health metrics health records idv IDV4GH ihme infoviz ipython iraq journal club machine learning malaria matching algorithms matchings MCMC media mortality mpld3 my rese...

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ORBi: Vandenplas Jérémie - Bayesian integration of external information into the single step appro... ach for genomically enhanced prediction of breeding values. User guide. Legal guide. Reference : Bayesian integration of external information into the single step approach for genomi... Title : Bayesian integration of external information into the single step approach for genomically enhanced prediction of breeding values. Author, co-author : Vandenplas, Jérémie. Event organizer : ADSA/ASAS. Keywords : Bayesian ; external information ; single step genomic procedure. However, current developments of genomic selection will bias evaluations because only records related to selected animals will be available. The single step genomic evaluation ssGBLUP could reduce pre-selection bias by the combination of genomic, pedigree and phenotypic information which are internal for the ssGBLUP. But, in opposition to multi-step methods, external information, i.e. information from outside ssGBLUP, like EBV and associated reliabilities from Multiple Across Country Evaluation which represent a priori known phenotypic informa...

http://orbi.ulg.ac.be/handle/2268/129989

Does God Exist? | AppBrain Android MarketDoes God Exist. AppBrain Android Market. AppBrain. Browse Apps. All apps. Does God Exist. Does God Exist. by Michael Borland. Contact. Does God Exist. This application helps you explore an age-old question, Does God Exist. You'll be asked to indicate how consistent each observation is with the existence and non-existence of God. In the end, the odds that God exists are computed. After you've done a calculation, you can share the results via email, Google+, etc. Facebook doesn't work, but that's Facebook's fault not mine. Some suggestions for use: 1. Questions you can try to answer using the app: 1. Is an indifferent or evil God more probable than an all-powerful, loving God. 2 Is a God with limited power more probable than an all-powerful God. Please use the Email developer link to send your suggestions. Mathematical note: the app uses Bayes' theorem to perform the calculations, with a prior probability of 50%. Note that the reason that each observation has two questions is that we need these two responses to...

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The Infinite Monkey Theorem Petite Sirah is Vegan Friendly - Barnivore vegan wine guide... The Infinite Monkey Theorem Petite Sirah is Vegan Friendly. http://theinfinitemonkeytheorem.com/contact/. Company email September 2013 : "We do not use any animal products with our wine, we use plant based enzymes and we use cellulose paper for filtering. Our fruit comes to us as grapes and sometimes as juice, in both cases there is no animal products introduced before it gets to our winery. The Infinite Monkey Theorem The Blind Watchmaker Red. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infinite Monkey Theorem Malbec - Magnum. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infinite Monkey Theorem Cabernet Franc - Magnum. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infinite Monkey Theorem Black Alley White. by The Infinite Monkey Theorem, USA Vegan Friendly. by The Infinite Monkey Theorem, USA Vegan Friendly. The Infin...

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Bayesian Gene Expression... S Bayesian models for sparse regression analysis of high dimensional data , paper , rejoinder. BMC Bioinformatics. journal page Bottolo L; Richardson S. Blangiardo M; Richardson S. BMC Bioinformatics. 2008 "Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods" Statistical Applications in Genetics and Molecular Biology: Vol. journal page 2007 Blangiardo M., Richardson S. Bochkina N., Richardson S. 2007 Tail posterior probability for inference in pairwise and multiclass gene expression data , Biometrics, journal page , with supplementary material Lau, J. J 2007 Bayesian Model Based Clustering Procedures Journal of Computational and Graphical Statistics vol: 16 , Pages: 525 - 558, journal page Lewin, A., Bochkina, N. and Richardson, S 2007 Fully Bayesian mixture model for differential gene expression: simulations and model checks. Statistical Applications in Genetics and Molecular Biology Vol. journal page BGmix software. Lewin, A., Richards...

http://bgx.org.uk/publications.html

Divergence Theorem Applications | RM.com ®Divergence Theorem Applications. Divergence Theorem Applications A selection of articles related to divergence theorem applications. Original articles from our library related to the Divergence Theorem Applications. Mind >> World Mind Herbs of Folklore and Modern Medicine: White Willow, Aloe Vera, and Garlic Many of the medicinal herbs we use today have been studied by modern science for application in today’s medical field. Body Mysteries >> Homeopathy Hypnosis: A Selected Bibliography Index of bibliography sections Selected Periodicals 14 entries Edited Overviews of General Theories of Hypnosis 5 entries Specific Topics Related to Research into Hypnosis. Remedies >> Remedies A Divergence Theorem Applications is described in multiple online sources, as addition to our editors' articles, see section below for printable documents, Divergence Theorem Applications books and related discussion. Suggested Pdf Resources Divergence Theorem Examples. Divergence Theorem Examples. Gauss' divergence theorem relates ...

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HyperparameterIn Bayesian statistics, a 'hyperparameter' is a parameter of a prior distribution ; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter 'p' of a Bernoulli distribution, then:. One may take a single value for a given hyperparameter, or one can iterate and take a probability distribution on the hyperparameter itself, called a hyperprior. One often uses a prior which comes from a parametric family of probability distributions – this is done partly for explicitness so one can write down a distribution, and choose the form by varying the hyperparameter, rather than trying to produce an arbitrary function, and partly so that one can 'vary' the hyperparameter, particularly in the method of ' conjugate prior s,' or for 'sensitivity analysis.'. When using a conjugate prior, the posterior distribution will be from the same family, but will have different hyperparameters, wh...

https://en.wikipedia.org/wiki/Hyperparameter

Pi Day Art Posters // Martin Krzywinski / Genome Sciences Center... resources. Gene Volume Control, Graphic Science Scientific American, Jun 2015. Nature Methods Points of View visualization column. Nature Methods Points of Significance statistics column. `pi` Day 2013 Art Posters. 2013 `pi` day. 2014 `pi` day. 2015 `pi` day. If you're not into details, you may opt to party on July 22nd, which is `pi` approximation day `\pi` 22/7. news + thoughts Association, correlation and causation Thu 01-10-2015 Correlation implies association, but not causation. Nature Methods Points of Significance column: Association, correlation and causation. ...more about the Points of Significance column Bayesian networks Thu 01-10-2015 For making probabilistic inferences, a graph is worth a thousand words. This month we continue with the theme of Bayesian statistics and look at Bayesian networks, which combine network analysis with Bayesian statistics. 2015 Points of Significance: Bayesian Statistics Nature Methods 12 :277-278. 2015 Points of Significance: Bayes' Theorem Nature Methods 12 :27...

http://mkweb.bcgsc.ca/pi/piday/

ORBi: Vandenplas Jérémie - Comparison and improvements of different Bayesian procedures to integra... te external information into genetic evaluations. O pen R epository and Bi bliography BICTEL/e. PoPuPS. Other OA projects at the ULg. Submitter guide. User guide. Legal guide. Tools box. FAQ. Glossary. Help. University of Liège. Library Network. Login. You are here:. ORBi. Detailled reference. Reference : Comparison and improvements of different Bayesian procedures to integrate external in... Document type : Scientific journals : Article. Discipline s : Life sciences : Genetics & genetic processes Life sciences : Animal production & animal husbandry. To cite this reference: http://hdl.handle.net/2268/103440. Title : Comparison and improvements of different Bayesian procedures to integrate external information into genetic evaluations. Language : English. Author, co-author : Vandenplas, Jérémie. Gengler, Nicolas. Publication date : Mar-2012. Journal title : Journal of Dairy Science. Publisher : American Dairy Science Association. Volume : 95. Issue/season : 3. Pages : 1513-1526. Peer reviewed : Yes verifie...

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Tutorial: Graphical Models and Bayesian Networks with R... Tutorial given at the useR. 2014 conference in Los Angeles Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark. Goals Introduce participants to using R for working with graphical models in particular graphical log-linear models for discrete data contingency tables and to probability propagation in Bayesian networks. Outline There will be a running example about building a probabilistic expert system for a medical diagnosis from real-world data. Probability propagation with Bayesian networks BNs and their implementation in the gRain gRaphical independence networks package. A look under the hood of BNs to understand mechanisms of probability propagation. Dependency graphs and conditional independence restrictions. Log-linear models, graphical models, decompsable models and their implementation in the gRim gRaphical independence models package. Model selection with gRim Converting a decompsable graphical model to a Bayesian network. Prerequisites Attendees are assumed to have a...

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.. Using Telemetry to recommend Add-ons for Firefox .. Post navigation .. Share this: .. Like thisThis post is about the beauty of a simple mathematical theorem and its applications in logic, machine learning and signal processing: Bayes’ theorem. During the way we will develop a simple recommender engine for Firefox add-ons based on data from Telemetry and reconstruct a signal from a noisy channel. tl;dr: If you couldn’t care less about mathematical beauty and probabilities, just have a look at the recommender system. Where P D|H is the

**likelihood**of the data D given our hypothesis H, i.e. how likely it is to see data D given that our hypothesis H is true, and P H is the prior belief about H, i.e. how likely our hypothesis H is true in the first place. According to logic, from the statement “if A is true then B is true” we can deduce that “if B is false then A is false”. So we can see that probabilistic reasoning can be applied to make logical deductions, i.e. Recommender Engine. Telemetry submissions contain the IDs of the add-ons of our users. We could then use the answer to suggest new add-ons to user...http://robertovitillo.com/2014/05/16/using-telemetry-to-recommend-add-ons-for-firefox/

Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent... risk factor. - UCL Discovery. UCL Discovery. UCL home » Library Services » Electronic resources » UCL Discovery Enter your search terms. Advanced search. UCL Theses. For everyone Open Access. About UCL Discovery. UCL e-theses guidelines. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor. Abstract Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used ...

http://discovery.ucl.ac.uk/1339896/

Hales Jewett Theorem | RM.com ®Hales Jewett Theorem. realmagick.com The shrine of knowledge. Hales Jewett Theorem Density version of Hales - Jewett theorem. Hales Jewett Theorem is described in multiple online sources, as addition to our editors' articles, see section below for printable documents, Hales Jewett Theorem books and related discussion. Suggested Pdf Resources The Hales - Jewett Theorem. Importance of HJT. • The Hales - Jewett theorem is presently one of the most useful techniques in Ramsey theory. www.ti.inf.ethz.ch. A new proof of the density Hales - Jewett theorem. RAMSEY THEORY: VAN DER WAERDEN'S THEOREM AND THE. A density version of the Hales - Jewett theorem - Stanford University The well known theorem of Hales and Jewett., Theorem 1. math.stanford.edu. The Hales - Jewett theorem. This chapter presents a deep combinatorial theorem by Hales and Jewett, stated Hales - Jewett theorem 7.2, from which the classical version follows easily. Suggested News Resources Heute in den Feuilletons Wahrscheinlichkeit eines verhängnis...

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Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation by Ming T. Tan, G... uo-Liang Tian, Kai Wang Ng - Kenward - 2010 - International Statistical Review - Wiley Online Library. Skip to Main Content Please log in or register to access this feature. Log in / Register. Log In. E-Mail Address. Password. Forgotten Password. Remember Me. Register. Institutional Login. Home. Statistics. Applied Probability Statistics. International Statistical Review. Vol 78 Issue 3. Abstract. JOURNAL TOOLS Get New Content Alerts. Get RSS feed. Save to My Profile. Get Sample Copy. Recommend to Your Librarian. JOURNAL MENU Journal Home FIND ISSUES Current Issue. All Issues FIND ARTICLES Early View. Most Accessed. Most Cited. GET ACCESS Subscribe / Renew. FOR CONTRIBUTORS OnlineOpen. Author Guidelines. Submit an Article. ABOUT THIS JOURNAL Society Information. News. Overview. Editorial Board. Permissions. Advertise. Contact. SPECIAL FEATURES Interview Papers. Discussion Papers. To Our Authors: Newsletter. Short Book Reviews Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computati...

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mcmc | bayesianbiologistmcmc. bayesianbiologist. bayesianbiologist Corey Chivers on P A|B ∝P B|A P A Menu Skip to content Home. About. Tag Archives: mcmc October 18, 2012. Introduction to Bayesian Methods guest lecture By Corey Chivers. Posted in Probability, Rstats, Teaching. Tagged bayesian, ecology, mcmc, methods, teaching. 7 Comments This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides. March 22, 2012. Montreal R Workshop: Introduction to Bayesian Methods By Corey Chivers. Posted in Probability, Rstats, Teaching. Tagged bayesian, mcmc, models, R user group, rstats, workshop. Leave a comment Monday, March 26, 2012 14h-16h, Stewart Biology N4/17. Corey Chivers, Department of Biology McGill University. This is a meetup of the Montreal R User Group. Be sure to join the group and RSVP. More information about the workshop here. T...

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.. Monthly Archives: November 2012 .. The Density Cluster Tree: A Guest Post .. WHAT IS BAYESIAN/The Density Cluster Tree: A Guest Post November 23, 2012 – 7:33 pm. This post is about the notion of consistency, the CD estimator, and its analysis. 4 Analysis. Posted in Uncategorized. WHAT IS BAYESIAN/FREQUENTIST INFERENCE. Nate Silver’s book, various comments on my blog, comments on other blogs, Sharon McGrayne’s book, etc have made it clear to me that there is still a lot of confusion about what Bayesian inference is and what Frequentist inference is. Frequentist inference and Bayesian Inference are defined by their goals, not their methods. Saying That Confidence Intervals Do Not Represent Degrees of Belief Is Not a Criticism of Frequentist Inference. Saying That Posterior Intervals Do Not Have Frequency Coverage Properties Is Not a Criticism of Bayesian Inference. And conversely, it is possible to do Bayesian inference without using Bayes’ theorem as Michael Goldstein, for example, has shown. As I will discuss in that review, Nate argues forcefully that Bayesian analysis is superior to Frequentist anal...

https://normaldeviate.wordpress.com/2012/11/

Bayesian approaches to brain function... 'Bayesian approaches to brain function' investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. It is frequently assumed that the nervous system maintains internal probabilistic model s that are updated by neural processing of sensory information using methods approximating those of Bayesian probability. N Rao Editor 2007, Bayesian Brain: Probabilistic Approaches to Neural Coding, The MIT Press; 1 edition Jan 1 2007 Knill David,Pouget Alexandre 2004, The Bayesian brain: the role of uncertainty in neural coding and computation,TRENDS in Neurosciences Vol.27 No.12 December 2004. Origins Psychophysics Neural coding Electrophysiology Predictive coding Free energy See also References External links. 2008 Was Helmholtz a Bayesian?" 'Perception' 39, 642–50 The basic idea is that the nervous system needs to organize sensory data into an accurate internal model of the outside world. During the 1990s researc...

https://en.wikipedia.org/wiki/Bayesian_approaches_to_brain_function

A Bayesian method for inferring quantitative information from FRET datadoi: 10.1186/2046-1682-4-10. For three measurements, one from each of the three spectral channels, the

**likelihood**is:. We then simulated three-cube FRET measurements, generating data for the donor, acceptor, and FRET channels Figure Figure2A, 2A, right. A typical three-cube FRET experiment is simulated from three virtual cells, each containing the indicated concentrations of donor- and acceptor- tagged proteins A, left. The approximate posterior probability distributions for K d and E fr are shown in Figure Figure2C. The approximate posterior probability distributions for K d A have different shapes if the data analyzed was simulated from three cells containing equal concentrations of donors and acceptors which are ... Figure Figure6 6 shows the approximate posterior probability distributions for E fr and K d that result from analyzing the same data in the presence and absence of prior information for E fr. As another form of prior information, we could also include error in the cal...http://pubmedcentralcanada.ca/pmcc/articles/PMC3126788/

"To Bayes or Not to Bayes? A Comparison of Two Classes of Models of Inf" by David V. Budescu and Hs"To Bayes or Not to Bayes. A Comparison of Two Classes of Models of Inf" by David V. Budescu and Hsiu-Ting Yu. Search. Browse Collections. My Account. Digital Commons Network™. DigitalResearch@Fordham. My Account. FAQ. Psychology. Faculty. Psychology Faculty Publications. To Bayes or Not to Bayes. A Comparison of Two Classes of Models of Information Aggregation. Authors. David V. Budescu, Fordham University University of Illinois at Urbana-Champaign at time of publication Hsiu-Ting Yu, University of Illinois at Urbana-Champaign. APA Citation: Budescu, D. V & Yu, H. To Bayes or not to Bayes. A comparison of two classes of models of information aggregation. Disciplines. Psychology. We model the aggregation process used by individual decision makers DMs who obtain probabilistic information from multiple, possibly nonindependent, sources. We distinguish between two qualitatively different aggregation approaches: compromising, by averaging the advisors’ opinions, and combining the forecasts according to a naïve im...

http://fordham.bepress.com/psych_facultypubs/40/

Structure Learning with Nonparametric Decomposable Models - Microsoft Research. Structure Learning with Nonparametric Decomposable Models - Microsoft Research. . Our research Connections Careers About us. Microsoft Translator. All Downloads Events Groups News People Projects Publications Videos. Structure Learning with Nonparametric Decomposable Models Anton Schwaighofer, Mathäus Dejori, Volker Tresp, and Martin Stetter 2007. Abstract We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be handled in a unified framework. Also, consistency of the underlying probabilistic model is guaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data. Details Publication type Inproceedings. Published in Artificial N...

http://research.microsoft.com/apps/pubs/default.aspx?id=74483

Designing Optimal Sequential Experiments for a Bayesian ClassifierDesigning Optimal Sequential ExperDesigning Optimal Sequential Experiments for a Bayesian Classifier. Designing Optimal Sequential Experiments for a Bayesian Classifier. The Community for Technology Leaders. CSDL. Institutions and Libraries. Resources. RSS Feeds. Terms of Use. Peer Review. Subscribe. CSDL Home. IEEE Transactions on Pattern Analysis & Machine Intelligence. Issue No.03 - March. Subscribe. Designing Optimal Sequential Experiments for a Bayesian Classifier. Issue No.03 - March 1999 vol.21. Robert Davis. Armand Prieditis. DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.754585. ABSTRACT. p b Abstract /b As computing power has grown, the trend in experimental design has been from techniques requiring little computation towards techniques providing better, more general results at the cost of additional computation. This paper continues this trend presenting three new methods for designing experiments. A summary of previous work in experimental design is provided and used to show how these new methods generalize previous c...

http://computer.org/csdl/trans/tp/1999/03/i0193-abs.html

cs343 p. 368cs p bayes theorem many of the methods used for dealing with uncertainty in expert systems are based on bayes theorem notation p a probability of event a p a b probability of events a and b occurring together p a b conditional probability of event a given that event b has occurred if a and b are independent then p a b p a expert systems usually deal with events that are not independent e g a disease and its symptoms are not independent bayes theorem p a b p a b p b p b a p a therefore p a b p b a p a p b contents nbsp nbsp nbsp page nbsp nbsp nbsp prev nbsp nbsp nbsp next nbsp nbsp nbsp page nbsp nbsp nbsp index nbsp nbsp nbsp...

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Don Bayes, Sr. - Obituaries - The Daily Telegram - Adrian, MI - Adrian, MI... e-edition. classifieds. cars. homes. FEATURED. official s foundation dissolved in Lenawee court ... Former Adrian man gets $120K grant for science research ... Meetings established for those impacted by dementia ... official s foundation dissolved in Lenawee court ... Former Adrian man gets $120K grant for science research ... Meetings established for those impacted by dementia ... Don Bayes, Sr. Don Bayes, Sr., age 62, of Adrian passed away peacefully, Monday, Dec. 16, 2013, at his home, surrounded by his loving family. Comment. The Daily Telegram - Adrian, MI. Posted Dec. Posted Dec. 17, 2013 at 10:17 AM ADRIAN. Don Bayes, Sr., age 62, of Adrian passed away peacefully, Monday, Dec. 16, 2013, at his home, surrounded by his loving family. 11, 1951, in Adrian, to Richard B. and Phyllis Fowle Bayes, Sr. In addition to his wife of 35 years, Renee, Don is survived by his three sons, Don Amy Bayes, Jr. of Ypsilanti, Doug Bayes and Coty Bayes of Adrian; two daughters, Karissa Kubacki of Adrian and Brieana Core...

http://lenconnect.com/article/20131217/OBITUARIES/131219223/-1/obituaries?rssfeed=true

SOSGSSD 2011 | The University of Western Ontariososgssd the university of western ontario southern ontario statistics graduate students seminar days the university of western ontario homepage accommodations registration poster advertisement contact summer school outline schedule seminar days outline submit an abstract list of presentations sponsors day summer school date may the summer workshop is a three day intensive workshop on spatial statistics for non gaussian data taught by patrick brown from cancer care ontario and virgilio gomez rubio from the university of castilla la mancha spain the material will cover advanced topics including the generalized linear geostatistical model spatial models for disease mapping bayesian inference using integrated nested laplace approximation markov random field approximations to geostatistical processes and methods for spatio temporal data the instruction will be evenly divided between lectures and supervised computer labs the material will be suitable for students with the equivalent of one year of graduate statisti...

http://stats.uwo.ca/gradwebs/jlee/sosgssd2011/ss_outline.php

.. Debunking Two Nate Silver MythsThere is nothing big data about Nate's methods. That's big data. That is big data. The fact is, he didn't need big data to do this. Look at it this way, if a meteorologist says there a 90% chance of rain where you live and it doesn't rain, the forecast wasn't necessarily wrong, because 10% of the time it shouldn't rain - otherwise the odds would be something other than a 90% chance of rain. In a frequentist interpretation, saying that an outcome of an event has a probability X% of occuring, you're saying that if you were to run an infinite series of repetitions of the event, then on average, the outcome would occur in X out of every 100 events. What it says is: for any specific event, it will have one outcome. But given the current state of information available to me , I can have a certain amount of certainty about whether or not the event will occur. It just means that given the current state of my knowledge, I expect a particular outcome, and the information I know gives me that degree of certainty. Bayesi...

http://goodmath.scientopia.org/tag/frequentist/

.. Dr. Bayes, or How I Learned to Stop Worrying and Love Updating .. Author .. Follow me on TwitteUpdating is really just a form of learning, but Bayes’ theorem gives us a way to structure that learning that turns out to be very powerful. A big part of my professional life involves using statistical models to forecast rare political events, and I am deeply frustrated by frequent encounters with people who dismiss statistical forecasts out of hand see here and here for previous posts on the subject .Â It’s probably unrealistic of me to think so, but I am hopeful that recognition of the intuitive nature and power of Bayesian updating might make it easier for skeptics to make use of my statistical forecasts and others like them. I’m a firm believer in the forecasting power of statistical models, so I usually treat a statistical forecast as my initial belief or prior, in Bayesian jargon and then only revise that forecast as new information arrives. When the statistical forecast diverges from your prior belief, Bayes’ theorem offers a structured but simple way to arrive at a new estimate.Â Experience shows tha...

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Computer experimentBackground Objectives Computer simulation modeling Gaussian process prior. Design of computer experiments Problems with massive sample sizes. 'Bias correction': Use physical data to correct for bias in the simulation. Computer simulation modeling. Modeling of computer experiments typically uses a Bayesian framework. The basic idea of this framework is to model the computer simulation as an unknown function of a set of inputs. It is natural to see the simulation as a deterministic function that maps these 'inputs' into a collection of 'outputs'. For some simulations, such as climate models, evaluation of the output for a single set of inputs can require millions of computer hours. Gaussian process prior. The typical model for a computer code output is a Gaussian process. Owing to the Bayesian framework, we fix our belief that the function f follows a Gaussian process, f \sim \operatorname{GP} m \cdot,C \cdot,\cdot, where m is the mean function and C is the covariance function. Popular mean

**functions**are low or...https://en.wikipedia.org/wiki/Computer_experiment

www.nutritionj.com - Tablewww nutritionj com table table change in prior probabilities of cafestol not affecting serum cholesterol to posterior probabilities using data of the present study and bayesian analysis prior probability prior odds yes no posterior odds posterior probability very strong x bayes factor strong x bayes factor equivocal x bayes factor weak x bayes factor very weak x bayes factor a priori probabilities were converted to a priori odds and multiplied by the minimum bayes factor the obtained a postiori odds were converted to a postiori probabilities bayes factor e to the power z where z is the z score corresponding to the p value for obtaining an effect of mmol l under the null hypothesis p value z score the minimum bayes factor boekschoten et al nutrition journal doi...

http://nutritionj.com/content/3/1/7/table/T3

Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistic... s. Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics. The Community for Technology Leaders. CSDL. Institutions and Libraries. Resources. RSS Feeds. Newsletter. Terms of Use. Peer Review. Login. Subscribe. CSDL Home. IEEE Transactions on Knowledge & Data Engineering. Issue No.04 - July/August. Subscribe. Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics. Issue No.04 - July/August 2000 vol.12. Daniel Nikovski. DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.868904. ABSTRACT. p b Abstract /b The paper discusses several knowledge engineering techniques for the construction of Bayesian networks for medical diagnostics when the available numerical probabilistic information is incomplete or partially correct. This situation occurs often when epidemiological studies publish only indirect statistics and when significant unmodeled conditional dependence exists in the problem domain. While nothing can ...

http://computer.org/csdl/trans/tk/2000/04/k0509-abs.html

Acquisition and Transformation of **Likelihood**s to Conditional Probabilities for Bayesian Networks... c skaaning f jensen u kjaerulff and a madsen when developing real world applications of bayesian networks one of the largest obstacles is the highly time consuming process of gathering probabilistic information this paper presents an efficient technique applied for gathering probabilistic information for the large sacso system for printing system diagnosis the technique allows the domain experts to provide their knowledge in an intuitive and efficient manner the knowledge is formulated in terms of

**likelihood**s calling for methods to transform it into conditional probabilities suitable for the bayesian network the paper outlines a general transformation method based on symbolic propagation in a junction tree this page is copyrighted by aaai all rights reserved your use of this site constitutes acceptance of all of aaai s terms and conditions and privacy policy...http://aaai.org/Library/Symposia/Spring/1999/ss99-04-005.php

Pubs.GISS: 2004 PublicationsNature, 432, 1014-1017, doi:10.1038/nature03174. J Geophys., 109, D10303, doi:10.1029/2003JD004173. J Geophys., 109, D04313, doi:10.1029/2003JD003527. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. J Geophys., 109, D10304, doi:10.1029/2003JD004174. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 3. J Geophys., 109, D10305, doi:10.1029/2003JD004175. Rossow, 2004: Neural network uncertainty assessment using Bayesian statistics: A remote sensing application. Cairns, and A. Marshak, 2004: Automated algorithm for remote sensing of atmospheric aerosols and trace gases using MFRSR measurements. SPIE, vol. 5571, 238, doi:10.1117/12.565306. Cairns, A.A. Geophys. Lett., 31, L04118, doi:10.1029/2003GL019105. Cairns, A.A. Sci., 61, 1024-1039, doi:10.1175/1520-0469 2004 061 1024:SPOAOT 2.0.CO;2. Miller, C. Rosenzweig, W. Sci., 1023, 125-141, doi:10.1196/annals.1319.005. Forest Meteorol. I...

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Merlise A Clyde... Professor of Statistical Science, Duke University. Home. Bio. Papers. Software. DSS. Duke. Research Interests Model uncertainty and choice in prediction and variable selection problems for linear, generalized linear models and multivariate models. Bayesian Model Averaging. Prior distributions for model selection and model averaging. Wavelets and adaptive non-parametric function estimation. Spatial statistics. Experimental design for nonlinear models. Applications in proteomics, bioinformatics, astro-statistics, air pollution and health effects, and environmental sciences. Recent Papers Iversen, E.S., Lipton, G., Clyde, M., Monteiro, A. 2014 Functional Annotation Signatures of Disease Susceptibility Loci Improve SNP Association Analysis. BMC Genomics 15:398 http://dx.doi.org/10.1186/1471-2164-15-398 Clyde, M. A and Iversen, E.S. 2013 Bayesian Model Averaging in the M-Open Framework. In ``Bayesian Theory and Applications' edited by P. Damien, P. Dellaportas, N.G. Polson and D.A. Stephens. Oxford University...

https://stat.duke.edu/~clyde/

WinBUGS... infobox software name winbugs logo image bugs logo gif screenshot caption collapsible author developer the bugs project released discontinued yes latest release version latest release date winbugs is statistical software for bayesian analysis using markov chain monte carlo mcmc methods it is based on the bugs b ayesian inference u sing g ibbs s ampling project started in it runs under microsoft windows though it can also be run on linux or mac using wine it was developed by the bugs project a team of uk researchers at the mrc biostatistics unit cambridge and imperial college school of medicine london the last version of winbugs was version released in august development is now focused on openbugs an open source version of the package winbugs remains available as a stable version for routine use but is no longer being developed references external links category statistical software category monte carlo software category windows only free software...

https://en.wikipedia.org/wiki/WinBUGS

PLOS Medicine: The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009Other Article Types. Article-Level Metrics. Article. New York data were used to estimate numerators for ICU and death, and two sources of data—medically attended cases in Milwaukee or self-reported influenza-like illness ILI in New York—were used to estimate ratios of symptomatic cases to hospitalizations. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% 95% credible interval 0.026%–0.096%, sCIR of 0.239% 0.134%–0.458%, and sCHR of 1.44% 0.83%–2.64%. By using data on medically attended and hospitalized cases of pH1N1 infection in Milwaukee and information from New York City on hospitalizations, intensive care use, and deaths, the researchers estimate that the proportion of US cases with symptoms that died the sCFR during summer 2009 was 0.048%. When the researchers used a different approach to estimate the total number of symptomatic cases—based on New Yorkers' self-reported incidence of influenza-like-illness from a telepho...

http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000207

complex analysis - Extension of Liouville's Theorem? - Mathematics Stack Exchangecomplex analysis - Extension of Liouville's Theorem. - Mathematics Stack Exchange. Mathematics Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. Mathematics Questions. Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Extension of Liouville's Theorem. Liouville's Theorem states that if a function is bounded and holomorphic on the complex plane i.e. bounded and entire, then it is a constant function. Can we use Liouville's Theorem to somehow conclude that $f$ is a constant function. Conan Wong Dec 5 '12 at 21:33. user27126 Dec 5 '12 at 22:13. A constant modulus on the closure of a domain gives that the function is constant. add a comment. I don't see how you could use Liouville's theorem to prove that, but it does follow from Cauchy-Riemann's equations. 2 = u 2 + v 2$ to show that both $u$ and $v$ must be constant on $D$. After that it...

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Réunion d'hiver SMC 2009HELENE MASSAM, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 A conjugate prior for discrete hierarchical loglinear models In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the loglinear parameters or the cell probabilities parameters is a major challenge. In this talk we define a flexible family of conjugate priors for the wide class of discrete hierarchical loglinear models which includes the class of graphical models. The talk is concerned with a procedure for testing whether this function belongs to a given parametric family. WEI NING, Bowling Green State University, Department of Mathematics and Statistics A Generalized Lambda Distribution GLD Change Point Model For the Detection of DNA Copy Number Variations in Array CGH Data In this talk, we study the detection of the multiple change points of parameters of generalized lambda distributions GLD. The derivation of SIC indicates that SIC serves as an asymptotic approximation to a transformatio...

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algorithms - Issue while applying Master Theorem - Mathematics Stack Exchange... Mathematics Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. Mathematics Questions. Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Issue while applying Master Theorem. I've read about the master theorem for solving recurrences in Introduction to Algorithms, but have a problem probably, due to misunderstanding while applying it in some cases. For example, having recurrence $T n = 5 T \frac{n}{3} + \Theta n 2 \log n $ and trying to apply this theorem I have: $a=5; b=3; f n =\Theta n 2 \log n $. But as I understand, $f n = \Theta n 2 \log n $ doesn't imply regularity of $f n $ and the master theorem is impossible to apply in this case. It would help if you told us what the master theorem says. Marek Feb 13 '13 at 14:07. Marek Feb 13 '13 at 14:31. Because $g n $ is regular, we can apply the master theorem to it and it gives us the same r...

http://math.stackexchange.com/questions/302082/issue-while-applying-master-theorem?answertab=active

pr.probability - The $\sigma > 0$ condition in the Central Limit Theorem - MathOverflowpr.probability - The $\sigma 0$ condition in the Central Limit Theorem - MathOverflow. more stack exchange communities. Stack Exchange. The $\sigma 0$ condition in the Central Limit Theorem. In the version of central limit theorem for strictly stationary but weakly dependent for instance $\alpha$-mixing with fast decaying mixing coefficient random variables $X_1, X_2, \cdots$, the theorem in this Wikipedia page states see also Billingsley 1995 Theorem 27.4 : Theorem. Suppose that $X_1, X_2, \cdots$ is stationary and $\alpha$-mixing with $\alpha_n = O n {−5} $ and that $\mathbb{E} X_n = 0$ and $\mathbb{E} X_n {12} \infty$. Denote $S_n = X_1 + \cdots + X_n$, then the limit $\sigma 2 = \lim_{n\to \infty} \mathbb{E} S_n 2/n$ exists, and if $\sigma \ne 0$ then $S_n/ \sigma \sqrt{n} $ converges in distribution to the standard Gaussian distribution $\mathcal{N} 0, 1 $. Then $X_1, X_2 \cdots$ is stationary and $\alpha$-mixing with $\alpha_n = 0$ for all $n \ge 2$. $$ In the above example though the right hand side be...

http://mathoverflow.net/questions/59951/the-sigma-0-condition-in-the-central-limit-theorem

Bayesian Networks and the Problem of Unreliable Instruments - PhilSci-Archive... Login. Search Browse Simple Search. Advanced Search. Browse by Subject. Browse by Year. Browse by Conferences/Volumes. Browse Open Access Journals. Latest Additions. About the Archive. Archive Policy. History. Help. FAQ. Journal Eprint Policies. Contact Us. Bayesian Networks and the Problem of Unreliable Instruments. Bovens, Luc and Hartmann, Stephan 2000 Bayesian Networks and the Problem of Unreliable Instruments. Preview. PDF Download 2214Kb. Preview. Abstract We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider i repeated measurements of a single testable consequence of the hypothesis, ii measurements of multiple testable consequences of the hypothesis, iii theoretical support for the reliability of the instrument, and iv calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and s...

http://philsci-archive.pitt.edu/95/

combinatorics - A consequence of Wilson's Theorem - Mathematics Stack Exchangecombinatorics - A consequence of Wilson's Theorem - Mathematics Stack Exchange. Stack Exchange. Help Center Detailed answers to any questions you might have. Mathematics Questions. A consequence of Wilson's Theorem. By Wilson's Theorem we know that $$ p-1. \equiv -1 \mod p.$$ A consequence of this is apparently $$ p- k+1 !k. \equiv -1 {k+1} \mod p$$ where $0 \leq k \leq p-1$. Then $ p-i \equiv -i \mod p.$ I'm interested in proceeding in the above way, but I'm not sure how to. 4 Answers 4 active oldest votes. Hint $\ $ An equivalent way to state Wilson's theorem is that any complete system of representatives of nonzero remainders mod $\,n\,$ has product $\equiv -1.\,$ In particular this is true for any sequence of $\,p\,$ consecutive integers, after removing its $\rm\color{#c00}{multiple}$ of $\,p.\,$ Your special case is the sequence $\, -k,\,-k\!+\!1,\ldots,-1,\require{cancel}\cancel{\color{#c00}0,} 1,2,\ldots, p\!-\!k\!-\!1,\,$ with product $ -1 k k!\, p\!-\!k\!-\!1 !\equiv -1.\ $ QED Remark $\ $ Th...

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ergodic theory - Shannon-McMillan-Breiman Theorem - MathOverflow... chat blog. MathOverflow. MathOverflow Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. MathOverflow Questions. Sign up. MathOverflow is a question and answer site for professional mathematicians. Shannon-McMillan-Breiman Theorem. up vote 4 down vote favorite 1. Does anyone know of an easy proof of Shannon-McMillan-Brieman Theorem. ergodic-theory share. improve this question. asked Nov 30 '11 at 14:30. Igor Rivin Nov 30 '11 at 14:36. I agree with Igor Rivin's comment: please try to phrase your question in a more useful and less subjective way. add a comment. 2 Answers 2 active oldest votes. Weiss, " The Shannon-McMillan-Breiman theorem for a class of amenable groups ", Israel J. improve this answer. add a comment. up vote 1 down vote. improve this answer. Igor Rivin 49.6k. Igor Rivin Nov 30 '11 at 19:37. Thank you very much Igor. add a comment. discard By posting your answer, you agree to the privacy policy and te...

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Cross-species transmission... 'Cross-species transmission CST ' is the phenomenon of transfer of viral infection from one species, usually a similar species, to another. Often seen in emerging viruses where one species transfers to another which in turn transfers to humans. Examples include HIV-AIDS, SARS, Ebola, Swine flu, rabies, and Bird flu. Faria NR, Suchard MA, Rambaut A, Streicker DG, Lemey P. Simultaneously reconstructing viral cross-species transmission history and identifying the underlying constraints. Philos Trans R Soc Lond B Biol Sci. 2013 Feb 4;368 1614. The exact mechanism that facilitates the transfer is unknown, however, it is believed that viruses with a rapid mutation rate are able to overcome host-specific immunological defenses. This can occur between species that have high contact rates. It can also occur between species with low contact rates but usually through an intermediary species. Host Phylogeny Constrains Cross-Species Emergence and Establishments of Rabies Virus in bats. Similarity between species, for...

https://en.wikipedia.org/wiki/Cross-species_transmission

probability - How do I find these bounds using Chebychev's inequality and the Central Limit Theorem?probability - How do I find these bounds using Chebychev's inequality and the Central Limit Theorem. - Mathematics Stack Exchange. Stack Exchange. Help Center Detailed answers to any questions you might have. Mathematics Questions. How do I find these bounds using Chebychev's inequality and the Central Limit Theorem. Find a lower bound using Chebychev's inequality. Approximate the value using the central limit theorem. Do you know what Chebychev's inequality says. I think I figured out finding the lower bound using Chebychev's inequality. The central limit theorem deals with zn converging with standard normal distribution. In what way can you think of your Gamma-distributed random variable as a sum of iid random variables. The central limit theorem says that the average of iid random variables $y i$ having mean $\mu$ and variance $\sigma 2$ converges to a normal distribution with mean $\mu$ and variance $\sigma 2/n$. You know that we can express the gamma-distribution random variable $X$ as $$...

http://math.stackexchange.com/questions/248144/how-do-i-find-these-bounds-using-chebychevs-inequality-and-the-central-limit-th

Bayesian Operational Modal Analysis... 'Bayesian Operational Modal Analysis' BAYOMA adopts a Bayesian system identification approach for Operational Modal Analysis OMA. Operational Modal Analysis OMA aims at identifying the modal properties natural frequencies, damping ratio s, mode shape s, etc. The input excitations to the structure are not measured but are assumed to be ' ambient ' 'broadband random'. In a Bayesian context, the set of modal parameters are viewed as uncertain parameters or random variables whose probability distribution is updated from the prior distribution before data to the posterior distribution after data. The peak s of the posterior distribution represents the most probable value s 'MPV' suggested by the data, while the spread of the distribution around the MPV reflects the remaining uncertainty of the parameters. Pros and Cons Methods Notes See also References. In the absence of input loading information, the identified modal properties from OMA often have significantly larger uncertainty or variability than their co...

https://en.wikipedia.org/wiki/Bayesian_Operational_Modal_Analysis

Site MapComplete CV Research interests Courses taught EPIB-682 Introduction to Bayesian Analysis in the Health Sciences EPIB-683 Intermediate Bayesian Analysis for the Health Sciences Previous courses taught EPIB-607 Principles of Inferential Statistics in Medicine EPIB-613 Introduction to Statistical Software EPIB-621 Data Analysis in the Health Sciences EPIB-651 Bayesian Analysis in Medicine EPIB-668 Introduction to Bayesian Analysis in the Health Sciences EPIB-669 Intermediate Bayesian Analysis for the Health Sciences EPIB-675 Bayesian Analysis in the Health Sciences Publications Methodological publications Bayesian Sample Size Change-point methods and applications Diagnostic testing Meta analysis Other topics Medical publications Allergy and immunology Asthma Cardiology Gastroenterology Osteoporosis Quality of Life Rheumatology Other Book Bayesian software Bayesian Sample Size Change-point methods and applications Diagnostic testing Diagnostic testing in Genetics Links. Courses taught EPIB-682 Introduction to Bay...

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October, 2013 | R-bloggers - Part 2October, 2013. R news and tutorials contributed by 573 R bloggers Home About RSS add your blog. Here you will find daily news and tutorials about R, contributed by over 573 bloggers. Data Scientist – Machine Learning in booking.com @ Amsterdam. Data Scientist – Analytics in booking.com @ Amsterdam. Hadley Wickham s Ask Me Anything on Reddit. Learn R interactively with our new Introduction to R tutorial. Monthly Archives: October 2013 Detecting an Unfair Die with Bayes’ Theorem October 30, 2013 By Eric Cai - The Chemical Statistician Introduction I saw an interesting problem that requires Bayes Theorem and some simple R programming while reading a bioinformatics textbook. The Problem The following question is. I have currently been doing this the Continue reading. What Hadley Wickham uses October 30, 2013 By David Smith You know Hadley Wickham as the inventor of the ggplot2 visualization phenomenon, the creator of time-saving R packages like plyr and lubridate, and the Chief Scientist at RStudio. Binomial conf...

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Deviance (statistics)In statistics, 'deviance' is a quality of fit statistic for a model that is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares of residuals in ordinary least squares to cases where model-fitting is achieved by maximum

**likelihood**. Definition See also Notes References External links. The deviance for a model 'M' 0, based on a dataset 'y', is defined as:. : D y = -2 \Big \log \big p y\mid\hat \theta 0 \big -\log \big p y\mid\hat \theta s \big \Big .\,. Here \hat \theta 0 denotes the fitted values of the parameters in the model 'M' 0, while \hat \theta s denotes the fitted parameters for the "full model" or "saturated model" : both sets of fitted values are implicitly**functions**of the observations 'y'. Here the 'full model' is a model with a parameter for every observation so that the data are fitted exactly. In particular, suppose that 'M 1 ' contains the parameters in 'M 2 ', and 'k' additional parameters. Then, under the null hypothesis that 'M 2 ' is ...https://en.wikipedia.org/wiki/Deviance_(statistics)

Dead Dog Bounce... The Non-English Olympic Team. The BBC article on the Team GB Olympic kit entertains your humble correspondent. People I respect have been writing that The Powers That Be are hostile to England and the English for some time. Posted by. Dead Dog Bounce. Email This. BlogThis. Share to Twitter. Share to Facebook. Share to Pinterest. That Rod Liddle article. A disgrace, that's what it is. I've read the Rod Liddle article, and it's a disgrace. So, what is a disgrace in my view. Posted by. Dead Dog Bounce. Email This. BlogThis. Share to Twitter. Share to Facebook. Share to Pinterest. Bayes Theorem Banned. Quick post: judge bans Bayes Theorem from evidence according to the Guardian. The classic teaching example is of a test for a rare disease. If the disease affects 1/100,000 and the false positive rate is 1/10,000, and the test is 100% reliable otherwise, the probability is that a positive outcome is a false positive is 90%+. Posted by. Dead Dog Bounce. Email This. BlogThis. Share to Twitter. Share to Facebook....

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co.combinatorics - Helm's improvement to Beck-Fiala theorem - MathOverflowco.combinatorics - Helm's improvement to Beck-Fiala theorem - MathOverflow. MathOverflow. MathOverflow Meta. more stack exchange communities. Stack Exchange. sign up log in tour. Help Center Detailed answers to any questions you might have. MathOverflow Questions. Sign up. MathOverflow is a question and answer site for professional mathematicians. Helm's improvement to Beck-Fiala theorem. up vote 3 down vote favorite 1. Beck-Fiala theorem states that if X is a finite set and H is any family of subsets of X, in which every vertex occurs in at most d sets of H, then there is a a function f:X->{ 1} such for every set S in H we have |sum x in S f x. There were two papers that improve the bound of 2d-2. The later improvement is due to Helm to 2d-4. co.combinatorics share. improve this question. Boris Bukh 4,431. add a comment. 2 Answers 2 active oldest votes. up vote 2 down vote. The author Martin Helm seems nowadays to be a faculty member in financial engineering at Baruch College, see this http://www.bar...

https://mathoverflow.net/questions/1856/helms-improvement-to-beck-fiala-theorem

R-Forge: Software Map... Project People. Log In. New Account. Home. My Page. Projects. Software Map Tag cloud. Project Tree. Project List. Project tree. Now limiting view to projects in the following categories: Topic :: Bayesian Statistics :: Specific Model Fitting. Programming Language :: R. Programming Language :: Tcl. Operating System :: OS Independent. Natural Language :: English. Development Status :: 4 - Beta. Topic Bayesian Statistics 52 projects Bioinformatics 155 projects Biostatistics Medical Statistics 69 projects Chemoinformatics 17 projects Cluster Analysis 43 projects Computational Physics 12 projects Connectivity 21 projects Database 28 projects Datasets 29 projects Design of Experiments Analysis of Experimental Data 17 projects Econometrics 67 projects Education 27 projects Environmetrics 43 projects Finance 69 projects Genetics 67 projects Graphical Models 9 projects Graphical User Interface 21 projects Graphics 78 projects High Performance Computing 31 projects Machine Learning 73 projects Marketing Business A...

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dblp: Chunping Wang... default search action combined dblp search. author search. venue search. publication search. Publications: search using CompleteSearch. Chunping Wang. export bibliography BibTeX. RIS. RDF. XML. dblp keys. dblp key: homepages/54/2715. ask others Google. Google Scholar. MS Academic Search. see FAQ. see FAQ. What is the meaning of the colors in the publication lists. view electronic edition via DOI. export record BibTeX. RIS. RDF. XML. dblp key: conf/wocc/WangZW13. ask others Google. Google Scholar. MS Academic Search. PubZone Weihong Wang,. Shusheng Zheng, Chunping Wang : Reprogramming for target nodes in WSNs. view electronic edition via DOI. export record BibTeX. RIS. RDF. XML. dblp key: conf/cso/ChenDW12. ask others Google. Google Scholar. MS Academic Search. PubZone Lin Chen,. Dayong Deng, Chunping Wang : F-Parallel Reducts in the Information View. view electronic edition via DOI. export record BibTeX. RIS. RDF. XML. dblp key: conf/fuzzIEEE/MeiSW12. ask others Google. Google Scholar. MS Academic Search...

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The Beast Lyrics by Accept... Accept Lyrics. The Beast Lyrics. Lyrics Depot is your source of lyrics to The Beast by Accept. Please check back for more Accept lyrics. The Beast Lyrics Artist: Accept Album: Death Row. My mind is razor sharp And I'm wild-cat-mean I'd like to shred your face A danger to you When I feel the urge Deep down inside I lose my human touch I'm like a killing machine I want to resist - but can't hold it back The beast is unleashed - it's got to attack Again and again the force is too strong It's breaching the chains I've got the beast inside Never ever trust in me I've got the beast inside Craving a victim - it's telling me to kill I look through evil eyes Prowling in the streets In the dead of night I'm a man on the hunt I know my soul is under siege Can't somebody stop me now 'cause I'm losing control I've got the beast inside Don't you trust in me I know it must have been the beast inside It's driving me to kill It's gotta be the beast - the beast - the beast The beast - the beast - the beast - deep down ins...

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INI : Abstracts : SCBW03 : Exact Bayesian inference and model selection for some infection modelsini abstracts scbw exact bayesian inference and model selection for some infection models the ini has a new website this is a legacy webpage please visit the new site to ensure you are seeing up to date information an isaac newton institute workshop stochastic computation for the analysis of ecological and epidemiological data exact bayesian inference and model selection for some infection models authors damian clancy university of liverpool philip d o neill university of nottingham abstract while much progress in the analysis of infectious disease data depends upon mcmc methodology the simpler and more exact method of rejection sampling can sometimes be very useful using examples of influenza data from a population divided into households this talk will illustrate the use of rejection sampling in model fitting use of an initial sample to improve the efficiency of the algorithm selection between competing models of differing dimensionality navigation workshop timetable stochastic computation for the analysis ...

http://www-old.newton.ac.uk/programmes/SCB/abstract3/clancy.html

Shockley–Ramo theoremshockley ramo theorem shockley ramo theorem the shockley ramo theorem allows one to easily calculate the instantaneous electric current induced by a charge moving in the vicinity of an electrode it is based on the concept that current induced in the electrode is due to the instantaneous change of electrostatic flux line s which end on the electrode not the amount of charge received by the electrode per second the theorem appeared in william shockley s paper currents to conductors induced by a moving point charge and a year later in simon ramo s paper entitled currents induced by electron motion the shockley ramo theorem states that the instantaneous current i induced on a given electrode due to the motion of a charge is given by i e v q v where q is the charge of the particle v is its instantaneous velocity and e v is the component of the electric field in the direction of v at the charge s instantaneous position under the following conditions charge removed given electrode raised to unit potential and all ot...

https://en.wikipedia.org/wiki/Shockley–Ramo_theorem

Church–Rosser theoremchurch rosser theorem church rosser theorem right px in mathematics and theoretical computer science the church rosser theorem states that when applying reduction rules to term s in the lambda calculus the ordering in which the reductions are chosen does not make a difference to the eventual result more precisely if there are two distinct reductions or sequences of reductions that can be applied to the same term then there exists a term that is reachable from both results by applying possibly empty sequences of additional reductions the theorem was proved in by alonzo church and j barkley rosser after whom it is named the theorem is symbolized by the diagram at right if term a can be reduced to both b and c then there must be a further term d possibly equal to either b or c to which both b and c can be reduced viewing the lambda calculus as an abstract rewriting system the church rosser theorem states that the reduction rules of the lambda calculus are confluent as a consequence of the theorem a term in the l...

https://en.wikipedia.org/wiki/Church–Rosser_theorem

.. Tag Archive .. Deducing the inverse theorem for the multidimensional Gowers norms from the one-dTag Archive. You are currently browsing the tag archive for the ‘Gowers uniformity norms’ tag. Deducing the inverse theorem for the multidimensional Gowers norms from the one-dimensional version. 24 July, 2015 in expository, math.CO. Tags: Ben Green, Freiman isomorphism, Gowers uniformity norms, Nikos Frantzikinakis. by Terence Tao. 1 comment. This week I have been at a Banff workshop “ Combinatorics meets Ergodic theory “, focused on the combinatorics surrounding SzemerÃ©di’s theorem and the Gowers uniformity norms on one hand, and the ergodic theory surrounding Furstenberg’s multiple recurrence theorem and the Host-Kra structure theory on the other. This was quite a fruitful workshop, and directly inspired the various posts this week on this blog. Incidentally, BIRS being as efficient as it is, videos for this week’s talks are already online. As mentioned in the previous two posts, Ben Green, Tamar Ziegler, and myself proved the following inverse theorem for the Gowers norms:. Theorem 1 Inverse theorem for ...

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Is there a simple way to prove Bertrand's postulate from the prime number theorem? - Mathematics StaIs there a simple way to prove Bertrand's postulate from the prime number theorem. - Mathematics Stack Exchange. Stack Exchange. Help Center Detailed answers to any questions you might have. Mathematics Questions. Is there a simple way to prove Bertrand's postulate from the prime number theorem. Is there a simple way to prove Bertrand's postulate from the prime number theorem. Is the prime number theorem really "stronger" than Bertrand's postulate, in the sense that assuming the former can simplify a proof of the latter. EDIT: I am specifically referring to this version of PNT: $\pi x \sim \frac{x}{\log x}$. prime-numbers analytic-number-theory share. To prove Chebyshev's theorem, one can start by finding the estimate $$ \frac{2 {2n}}{2n+1} \binom{2n}{n} 2 {2n}, \tag{1} $$ then using it to conclude that $\vartheta x /x 4\log 2$ and hence $$ \limsup_{x \to \infty} \frac{\pi x }{x/\log x} = \limsup_{x \to \infty} \frac{\vartheta x }{x} \leq 4 \log 2, $$ and $\psi x /x x-2 \log 2/x - \log x+1 /x$ and hen...

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.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

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.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

http://bayesianbiologist.com/2012/10/18/introduction-to-bayesian-methods-guest-lecture/?like=1&_wpnonce=e775df7899

.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

http://bayesianbiologist.com/2012/10/18/introduction-to-bayesian-methods-guest-lecture/?like=1&source=post_flair&_wpnonce=178c498074

.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

http://bayesianbiologist.com/2012/10/18/introduction-to-bayesian-methods-guest-lecture/?like=1&source=post_flair&_wpnonce=8ac41143de

.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

http://bayesianbiologist.com/2012/10/18/introduction-to-bayesian-methods-guest-lecture/?like=1&source=post_flair&_wpnonce=9e4b539e19

.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

http://bayesianbiologist.com/2012/10/18/introduction-to-bayesian-methods-guest-lecture/?like=1&source=post_flair&_wpnonce=c7a699f16c

.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to bayesian lecture accompanying handouts and demos october introduction to bayesian methods guest lecture by corey chivers posted in probability rstats teaching tagged bayesian ecology mcmc methods teaching comments this is a talk i gave this week in advanced biostatistics at mcgill the goal was to provide an gentle introduction to bayesian methodology and to demonstrate how it is used for inference and prediction there is a link to an accompanying r script in the slides share this twitter facebook reddit google like this like loading related...

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Newton Institute Seminar : Pratola, M, 09/09/2011... The INI has a new website. DAE. Seminars. Pratola, M. DAE Seminar. Bayesian Calibration of Computer Model Ensembles Pratola, M Los Alamos National Laboratory Friday 09 September 2011, 11:30-12:00 Seminar Room 1, Newton Institute. Abstract Using field observations to calibrate complex mathematical models of a physical process allows one to obtain statistical estimates of model parameters and construct predictions of the observed process that ideally incorporate all sources of uncertainty. Many of the methods in the literature use response surface approaches, and have demonstrated success in many applications. However there are notable limitations, such as when one has a small ensemble of model runs where the model outputs are high dimensional. In such instances, arriving at a response surface model that reasonably describes the process can be dicult, and computational issues may also render the approach impractical. In this talk we present an approach that has numerous beneifts compared to some popular m...

http://www-old.newton.ac.uk/programmes/DAE/seminars/2011090911301.html

Bayesian vector autoregression... in statistics bayesian vector autoregression bvar uses bayesian methods to estimate a vector autoregression var in that respect the difference with standard var models lies in the fact that the model parameters are treated as random variable s and prior probabilities are assigned to them vector autoregressions are flexible statistical models that typically include many free parameters given the limited length of standard macroeconomic datasets bayesian methods have become an increasingly popular way of dealing with this problem of over parameterization the general idea is to use informative priors to shrink the unrestricted model towards a parsimonious naïve benchmark thereby reducing parameter uncertainty and improving forecast accuracy see for a survey a typical example is the shrinkage prior proposed by robert litterman and subsequently developed by other researchers at university of minnesota which is known in the bvar literature as the minnesota prior the informativeness of the prior can be set by t...

https://en.wikipedia.org/wiki/Bayesian_vector_autoregression

Laplace's Rule of Succession | Mathematical Association of AmericaLaplace s Rule of Succession. Membership. Member Discount Programs. Portrait Gallery. Guidelines for Convergence Authors. MAA Library Recommendations. Calendar of Events. MAA Distinguished Lecture Series. Past MAA Distinguished Lectures. MAA MathFest. Mathematical Sessions. Other Mathematical Sessions. Undergraduate Student Activities. SIGMAA Activities. Joint Mathematics Meetings. MAA Policies. MAA Section Meetings. You are here Home » Programs » Faculty and Departments » Course Communities » Laplace s Rule of Succession Laplace s Rule of Succession. Related Resources: Buffon s Needle Problem - Expository introduction Bernoulli Trials: Introduction Conditional Probability and Independent Events Conditional Probability Bayes Theorem Bayesian odds Expectation - Introduction Bayes Ratio: Dramatic Taxicab Example Dependent and Independent Events Principle of Proportionality Independent Events and Independent Experiments Conditional Recurrence Construction of Pascal s Triangle. Browse Classroom Capsules and Notes...

http://maa.org/programs/faculty-and-departments/course-communities/laplaces-rule-of-succession

Favard's theoremfavard s theorem favard s theorem in mathematics favard s theorem also called the shohat favard theorem states that a sequence of polynomials satisfying a suitable term recurrence relation is a sequence of orthogonal polynomials the theorem was introduced in the theory of orthogonal polynomials by and though essentially the same theorem was used by stieltjes in the theory of continued fraction s many years before favard s paper and was rediscovered several times by other authors before favard s work statement suppose that y y is a sequence of polynomials where y n has degree n if this is a sequence of orthogonal polynomials for some positive weight function then it satisfies a term recurrence relation favard s theorem is roughly a converse of this and states that if these polynomials satisfy a term recurrence relation of the form y n x c n y n d n y n for some numbers c n and d n then the polynomials y n form an orthogonal sequence for some linear function λ with λ in other words λ y m y n if m n the linear f...

https://en.wikipedia.org/wiki/Favard's_theorem

Tverberg's theoremtverberg s theorem tverberg s theorem image tverberg heptagon svg in discrete geometry tverberg s theorem first stated by is the result that sufficiently many points in d dimensional euclidean space can be partitioned into subset s with intersecting convex hull s specifically for any set of d r points there exists a point x not necessarily one of the given points and a partition of the given points into r subsets such that x belongs to the convex hull of all of the subsets the partition resulting from this theorem is known as a tverberg partition examples for r tverberg s theorem states that any d points may be partitioned into two subsets with intersecting convex hulls this special case is known as radon s theorem in this case for points in general position there is a unique partition the case r and d states that any seven points in the plane may be partitioned into three subsets with intersecting convex hulls the illustration shows an example in which the seven points are the vertices of a regular heptagon ...

https://en.wikipedia.org/wiki/Tverberg's_theorem

.. Post navigation .. Introduction to Bayesian Methods guest lecture .. Share this: .. Like this:Post navigation. ← Dark matter benchmarks: All over the map. Introduction to Bayesian lecture: Accompanying handouts and demos →. October 18, 2012 Introduction to Bayesian Methods guest lecture. By Corey Chivers ¶. Posted in Probability, Rstats, Teaching. ¶. Tagged bayesian, ecology, mcmc, methods, teaching. ¶. 7 Comments. This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides. . Share this:. Twitter Facebook Reddit Google. Like this:. Like Loading... Related....

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**Decoding methods**: In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords.

**Hyperparameter**: In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.

**Clonal Selection Algorithm**: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.

**Interval boundary element method**: Interval boundary element method is classical boundary element method with the interval parameters.

**Inverse probability weighting**: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.

**Vladimir Andreevich Markov**: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.

**Monte Carlo methods for option pricing**: In mathematical finance, a Monte Carlo option model uses Monte Carlo methods Although the term 'Monte Carlo method' was coined by Stanislaw Ulam in the 1940s, some trace such methods to the 18th century French naturalist Buffon, and a question he asked about the results of dropping a needle randomly on a striped floor or table. See Buffon's needle.

**Negative probability**: The probability of the outcome of an experiment is never negative, but quasiprobability distributions can be defined that allow a negative probability for some events. These distributions may apply to unobservable events or conditional probabilities.

**Matrix model**: == Mathematics and physics ==

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**An evaluation of elongation factor 1 alpha as a phylogenetic marker for eukaryotes.**

Elongation factor 1 alpha (EF-1 alpha) is a highly conserved ubiquitous protein involved in translation that has been suggested to have desirable properties for phylogenetic inference. To examine the utility of EF-1 alpha as a phylogenetic marker for eukaryotes, we studied three properties of EF-1 alpha trees: congruency with other phyogenetic markers, the impact of species sampling, and the degree of substitutional saturation occurring between taxa. Our analyses indicate that the EF-1 alpha tree is congruent with some other molecular phylogenies in identifying both the deepest branches and some recent relationships in the eukaryotic line of descent. However, the topology of the intermediate portion of the EF-1 alpha tree, occupied by most of the protist lineages, differs for different phylogenetic methods, and bootstrap values for branches are low. Most problematic in this region is the failure of all phylogenetic methods to resolve the monophyly of two higher-order protistan taxa, the Ciliophora and the Alveolata. JACKMONO analyses indicated that the impact of species sampling on bootstrap support for most internal nodes of the eukaryotic EF-1 alpha tree is extreme. Furthermore, a comparison of observed versus inferred numbers of substitutions indicates that multiple overlapping substitutions have occurred, especially on the branch separating the Eukaryota from the Archaebacteria, suggesting that the rooting of the eukaryotic tree on the diplomonad lineage should be treated with caution. Overall, these results suggest that the phylogenies obtained from EF-1 alpha are congruent with other molecular phylogenies in recovering the monophyly of groups such as the Metazoa, Fungi, Magnoliophyta, and Euglenozoa. However, the interrelationships between these and other protist lineages are not well resolved. This lack of resolution may result from the combined effects of poor taxonomic sampling, relatively few informative positions, large numbers of overlapping substitutions that obscure phylogenetic signal, and lineage-specific rate increases in the EF-1 alpha data set. It is also consistent with the nearly simultaneous diversification of major eukaryotic lineages implied by the "big-bang" hypothesis of eukaryote evolution. (+info)

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**Unusually high evolutionary rate of the elongation factor 1 alpha genes from the Ciliophora and its impact on the phylogeny of eukaryotes.**

The elongation factor 1 alpha (EF-1 alpha) has become widely employed as a phylogenetic marker for studying eukaryotic evolution. However, a disturbing problem, the artifactual polyphyly of ciliates, is always observed. It has been suggested that the addition of new sequences will help to circumvent this problem. Thus, we have determined 15 new ciliate EF-1 alpha sequences, providing for a more comprehensive taxonomic sampling of this phylum. These sequences have been analyzed together with a representation of eukaryotic sequences using distance-, parsimony-, and likelihood-based phylogenetic methods. Such analyses again failed to recover the monophyly of Ciliophora. A study of the substitution rate showed that ciliate EF-1 alpha genes exhibit a high evolutionary rate, produced in part by an increased number of variable positions. This acceleration could be related to alterations of the accessory functions acquired by this protein, likely to those involving interactions with the cytoskeleton, which is very modified in the Ciliophora. The high evolutionary rate of these sequences leads to an artificial basal emergence of some ciliates in the eukaryotic tree by effecting a long-branch attraction artifact that produces an asymmetric topology for the basal region of the tree. The use of a maximum-likelihood phylogenetic method (which is less sensitive to long-branch attraction) and the addition of sequences to break long branches allow retrieval of more symmetric topologies, which suggests that the asymmetric part of the tree is most likely artifactual. Therefore, the sole reliable part of the tree appears to correspond to the apical symmetric region. These kinds of observations suggest that the general eukaryotic evolution might have consisted of a massive radiation followed by an increase in the evolutionary rates of certain groups that emerge artificially as early branches in the asymmetric base of the tree. Ciliates in the case of the EF-1 alpha genes would offer clear evidence for this hypothesis. (+info)

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**Interaction of process partitions in phylogenetic analysis: an example from the swallowtail butterfly genus Papilio.**

In this study, we explored how the concept of the process partition may be applied to phylogenetic analysis. Sequence data were gathered from 23 species and subspecies of the swallowtail butterfly genus Papilio, as well as from two outgroup species from the genera Eurytides and Pachliopta. Sequence data consisted of 1,010 bp of the nuclear protein-coding gene elongation factor-1 alpha (EF-1 alpha) as well as the entire sequences (a total of 2,211 bp) of the mitochondrial protein-coding genes cytochrome oxidase I and cytochrome oxidase II (COI and COII). In order to examine the interaction between the nuclear and mitochondrial partitions in a combined analysis, we used a method of visualizing branch support as a function of partition weight ratios. We demonstrated how this method may be used to diagnose error at different levels of a tree in a combined maximum-parsimony analysis. Further, we assessed patterns of evolution within and between subsets of the data by implementing a multipartition maximum-likelihood model to estimate evolutionary parameters for various putative process partitions. COI third positions have an estimated average substitution rate more than 15 times that of EF-1 alpha, while COII third positions have an estimated average substitution rate more than 22 times that of EF-1 alpha. Ultimately, we found that although the mitochondrial and nuclear data were not significantly incongruent, homoplasy in the fast-evolving mitochondrial data confounded the resolution of basal relationships in the combined unweighted parsimony analysis despite the fact that there was relatively strong support for the relationships in the nuclear data. We conclude that there may be shortcomings to the methods of "total evidence" and "conditional combination" because they may fail to detect or accommodate the type of confounding bias we found in our data. (+info)

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**Diagnosing anaemia in pregnancy in rural clinics: assessing the potential of the Haemoglobin Colour Scale.**

Anaemia in pregnancy is a common and severe problem in many developing countries. Because of lack of resources and staff motivation, screening for anaemia is often solely by clinical examination of the conjunctiva or is not carried out at all. A new colour scale for the estimation of haemoglobin concentration has been developed by WHO. The present study compares the results obtained using the new colour scale on 729 women visiting rural antenatal clinics in Malawi with those obtained by HemoCue haemoglobinometer and electronic Coulter Counter and with the assessment of anaemia by clinical examination of the conjunctiva. Sensitivity using the colour scale was consistently better than for conjunctival inspection alone and interobserver agreement and agreement with Coulter Counter measurements was good. The Haemoglobin Colour Scale is simple to use, well accepted, cheap and gives immediate results. It shows considerable potential for use in screening for anaemia in antenatal clinics in settings where resources are limited. (+info)

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**Laboratory assay reproducibility of serum estrogens in umbilical cord blood samples.**

We evaluated the reproducibility of laboratory assays for umbilical cord blood estrogen levels and its implications on sample size estimation. Specifically, we examined correlation between duplicate measurements of the same blood samples and estimated the relative contribution of variability due to study subject and assay batch to the overall variation in measured hormone levels. Cord blood was collected from a total of 25 female babies (15 Caucasian and 10 Chinese-American) from full-term deliveries at two study sites between March and December 1997. Two serum aliquots per blood sample were assayed, either at the same time or 4 months apart, for estrone, total estradiol, weakly bound estradiol, and sex hormone-binding globulin (SHBG). Correlation coefficients (Pearson's r) between duplicate measurements were calculated. We also estimated the components of variance for each hormone or protein associated with variation among subjects and variation between assay batches. Pearson's correlation coefficients were >0.90 for all of the compounds except for total estradiol when all of the subjects were included. The intraclass correlation coefficient, defined as a proportion of the total variance due to between-subject variation, for estrone, total estradiol, weakly bound estradiol, and SHBG were 92, 80, 85, and 97%, respectively. The magnitude of measurement error found in this study would increase the sample size required for detecting a difference between two populations for total estradiol and SHBG by 25 and 3%, respectively. (+info)

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**Maximum-likelihood generalized heritability estimate for blood pressure in Nigerian families.**

Elevated blood pressure (BP) is more common in relatives of hypertensives than in relatives of normotensives, indicating familial resemblance of the BP phenotypes. Most published studies have been conducted in westernized societies. To assess the ability to generalize these estimates, we examined familial patterns of BP in a population-based sample of 510 nuclear families, including 1552 individuals (320 fathers, 370 mothers, 475 sons, and 387 daughters) from Ibadan, Nigeria. The prevalence of obesity in this community is low (body mass index: fathers, 21.6; mothers, 23.6; sons, 19.2; and daughters=21.0 kg/m2). The BP phenotype used in all analyses was created from the best regression model by standardizing the age-adjusted systolic blood pressure (SBP) and diastolic blood pressure (DBP) to 0 mean and unit variance. Heritability was estimated by use of the computer program SEGPATH from the most parsimonious model of "no spouse and neither gender nor generation difference" as 45% for SBP and 43% for DBP. The lack of a significant spouse correlation is consistent with little or no influence of the common familial environment. However, the heritability estimate of <50% for both SBP and DBPs reinforces the importance of the nonshared environmental effect. (+info)

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**A gene for X-linked idiopathic congenital nystagmus (NYS1) maps to chromosome Xp11.4-p11.3.**

Congenital nystagmus (CN) is a common oculomotor disorder (frequency of 1/1,500 live births) characterized by bilateral uncontrollable ocular oscillations, with onset typically at birth or within the first few months of life. This condition is regarded as idiopathic, after exclusion of nervous and ocular diseases. X-linked, autosomal dominant, and autosomal recessive modes of inheritance have been reported, but X-linked inheritance is probably the most common. In this article, we report the mapping of a gene for X-linked dominant CN (NYS1) to the short arm of chromosome X, by showing close linkage of NYS1 to polymorphic markers on chromosome Xp11.4-p11.3 (maximum LOD score of 3.20, over locus DXS993). Because no candidate gene, by virtue of its function, has been found in this region of chromosome Xp, further studies are required, to reduce the genetic interval encompassing the NYS1 gene. It is hoped that the complete gene characterization will address the complex pathophysiology of CN. (+info)

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**A note on power approximations for the transmission/disequilibrium test.**

The transmission/disequilibrium test (TDT) is a popular method for detection of the genetic basis of a disease. Investigators planning such studies require computation of sample size and power, allowing for a general genetic model. Here, a rigorous method is presented for obtaining the power approximations of the TDT for samples consisting of families with either a single affected child or affected sib pairs. Power calculations based on simulation show that these approximations are quite precise. By this method, it is also shown that a previously published power approximation of the TDT is erroneous. (+info)

### What is the likelihood that STD transmission wont occur the first time you have unprotected sex?

What is the likelihood that STD transmission wont occur the first time you have unprotected sex?

Is there any reason to continue using condoms?

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You never know. The risk is higher depending on the amount of partners you have, but it can also be just one time and you get one. Its unpredictable. I just think its stupid to risk, cause the stds you can get are not a joke, and you will have to stick with them forever. You would be filled up with antibiotics. But yeah, thats your choice. Its like saying, what is the likelihood of me crossing the street when the green lights are on, and getting killed! Lot of factors are involved. But you can't just depend on luck. You can be lucky once, but the next NO. It would be justified in the 1960s when HIV wasnt known, but now you aware it does exist, and you have a way to prevent it from affecting you. Why not use it? Seems to me as pure suicide.

### What is the likelihood of someone getting pregnant during the 2-4th day or her period, while on birth control?

What is the likelihood of someone getting pregnant during the 2-4th day or her period, while on birth control?

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The likelihood of pregnancy is the same every day on the pill. As long as she took all her pills correctly, it is less than a one percent chance she is pregnant. Of course, it is always possible because no birth control method is 100%, but it is very unlikely.

### What is the functions of Vincristine and Doxorubicin and why can they be fatal to a cell?

What is the functions of Vincristine and Doxorubicin and why can they be fatal to a cell?

Subject: chemotherapeudic drugs and celllular actions

.

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They stop cell division to stop spread of cancer

### What is the likelihood of having a heart attack after an attack of pectoral angina?

My grandfather had an angina attack on the weekend and I was just wondering the likelihood of him now having a heart attack. And also if it is highly likely how soon after the angina attack?

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ask a cardiologist.

### What is the likelihood of the tests being wrong?

What is the likelihood of the tests being wrong?

17 dpo- took early results and one-step cvs test and both negative. (they test more sensitive)

i show no signs or being pregnant. no period either. what are the chances of the tests being wrong?

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I am going through the same thing right now. With my first pregnancy, it took about 12 weeks for it to show up. I don't know how many kits I wasted money on. Best bet is to see the Dr. for blood work if you're that worried. Good luck!

### What is the likelihood of offspring developing schizophrenia from their parents?

If a mother developed schizophrenia what is the likelihood of her children developing it? Can it be prevented?

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According to E. Fuller Torey M.D's book "Surviving Schizophrenia" it is 13%

### What is the likelihood of wound healing following a 3rd degree and second degree burn?

3rd degree = full thickness burn

2nd degree = partial thickness burn

Need to know the likelihood of wound healing in each case.

Thanks in advance.

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well likelihood of 2nd degree burns of healing is 100%, may leave scars though. 3rd degree depends. 3rd degree burns actually is a type of burn that starts to cook the muscle. It's possible you could lose that part of your body for 3rd degrees.

### What is the likelihood of me getting pregnant on the day I ovulate?

Barring any unforseen issues, what is the likelihood that I have become pregnant being that my husband and I had sex on the day that according to my cycle, I was to be ovulating? We have been trying for almost 8 months and it has sort of become frustrating. Also, how much do you believe that stress plays a role in difficulties conceiving?

Thank you.

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well of all the days in the month the day before and day of ovulation are the best days so i'd say excellent