**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.

**Mathematical Concepts**: Numeric or quantitative entities, descriptions, properties, relationships, operations, and events.

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

**Mathematics**: The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**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.

**Information Theory**: An interdisciplinary study dealing with the transmission of messages or signals, or the communication of information. Information theory does not directly deal with meaning or content, but with physical representations that have meaning or content. It overlaps considerably with communication theory and CYBERNETICS.

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

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

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

**Enzymes**: Biological molecules that possess catalytic activity. They may occur naturally or be synthetically created. Enzymes are usually proteins, however CATALYTIC RNA and CATALYTIC DNA molecules have also been identified.

**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.

**Achillea**: A plant genus of the family ASTERACEAE that has long been used in folk medicine for treating wounds.

**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.

**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.

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... 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

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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

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... 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

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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

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doi: 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...

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. 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...

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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

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.

Updating is really just a form of learning, but

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

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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...

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HELENE MASSAM, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 A conjugate prior for discrete hierarchical loglinear models In

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... '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...

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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 ...

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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

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... EPIB 607 Principles of Inferential Statistics in Medicine. Prerequisite: Basic understanding of differentiable and integral calculus. Enrollment in the Epidemiology program at McGill University. Objectives:. The aim of this course is to provide students with basic principles of statistical inference applicable to clinical and epidemiologic so that they can: i understand how statistical methods are used by others, ii apply statistical methods in their own research, and iii use the methods learned in this course as a foundation for more advanced biostatistics courses. Content: Topic:. Baldi Moore:. Data collection, description, and display. Chapters 1, 2. . Probability, Discrete and continuous distributions Randomness and random variables

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normalized random measures driven by increasing additive processes kent academic repository login admin simple search advanced search home browse latest additions help contact normalized random measures driven by increasing additive processes nieto barajas luis e and prunster igor and walker stephen g normalized random measures driven by increasing additive processes annals of statistics pp issn full text available pdf normalized random measures download kb preview official url http dx doi org abstract this paper introduces and studies a new class of nonparametric prior distributions random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes in particular we present results for the distribution of means under both prior and posterior conditions and via the use of strategic latent variables undertake a ful...

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**theorem**and its applications in logic, machine ... signal processing:**Bayes**’**theorem**. During the way we will ... application of the**theorem**to find out why many science research ... 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 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 probabilis...http://robertovitillo.com/2014/05/16/using-telemetry-to-recommend-add-ons-for-firefox/

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**Bayes**'**Theorem**. Many of the methods used for ... are based on**Bayes**'**Theorem**. Notation:. P A. ... not independent.**Bayes**'**Theorem**. P A B = P A. B * P B = P B ... 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...http://cs.utexas.edu/~novak/cs343368.html

Identifying sites under positive selection with uncertain parameter estimates.of an empirical

**Bayes**or by a**Bayes**empirical**Bayes**approach ... a previous full-**Bayes**approach to include models with high ... show that i full**Bayes**can be superior to empirical**Bayes**when...http://biomedsearch.com/nih/Identifying-sites-under-positive-selection/16936785.html

**ian inference in marketing****Bayes****Bayes**ian_inference_in_marketing**Bayes**ian_inference_in_marketing. thumb|alt= ... text|**Bayes**'**Theorem**In marketing ,**Bayes**ian inference ... is known as a**Bayes**ian probability. “**Bayes**ian Statistics ...**Bayes**ian inference in marketing**Bayes**ian inference in marketing. thumb|alt=text|**Bayes**'**Theorem**In marketing,**Bayes**ian inference allows for decision making and market research evaluation under uncertainty and with limited data. Such a probability is known as a**Bayes**ian probability. “**Bayes**ian Statistics and Marketing Research”, Journal of the Royal Statistical Society, Series C 15 3 : 173-190.**Bayes**ian inference allows for decision making and market research evaluation under uncertainty and limited data.**Bayes**ian probability specifies that there is some prior probability. To sum up this formula: the posterior probability of the hypothesis is equal to the prior probability of the hypothesis multiplied by the conditional probability of the evidence given the hypothesis, divided by the probability of the new evidenc...https://en.wikipedia.org/wiki/Bayesian_inference_in_marketing

Classification ruledisease. We can use

**Bayes**'**theorem**to calculate the probability ... Testing classification rules Binary and multiclass classification Table of Confusion False positives. For example, a medical test for a disease may return a positive result indicating that patient has a disease even if the patient does not have the disease. We can use**Bayes**'**theorem**to calculate the probability that a positive test result is a false positive. Then, the probability that the patient actually has the disease given the positive test result is. A P A + P B |\text{not }A P \text{not }A } \\ \\ &= \frac{0.99\times 0.001}{0.99 \times 0.001 + 0.05\times 0.999} \\ ~\\ &\approx 0.019. and hence the probability that a positive result is a false positive is about 1 0.019 = 0.98, or 98%. If the test reported a negative result in patients without the disease with probability 0.999, then. so that 1 0.5 = 0.5 now is the probability of a false positive. For example, a medical test for a disease may return a negative result indicating that pat...https://en.wikipedia.org/wiki/Classification_rule

**Bayes****theorem**(disambiguation)**bayes****theorem**disambiguation**bayes****theorem**... disambiguation**bayes****theorem**may refer to**theorem****bayes****theorem**a**theorem**which expresses how a ...**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****bayes**ian 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**bayes**ian 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)

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Novel health economic evaluation of a vaccination strategy to prevent HPV-related diseases: the BESstrategy within a

**Bayes**ian framework. Type: Article. Title: ... Adolescent, Adult,**Bayes****Theorem**, Child, Cost-Benefit Analysis, ... Novel health economic evaluation of a vaccination strategy to prevent HPV-related diseases: the BEST study. - UCL Discovery. UCL Discovery. UCL home » Library Services » Electronic resources » UCL Discovery Enter your search terms. Advanced search. Browse by:. Department. UCL Theses. Latest. For everyone Open Access. About UCL Discovery. UCL Press. For UCL authors Deposit your research. How to deposit. Open Access funding. UCL e-theses guidelines. Contact us. Novel health economic evaluation of a vaccination strategy to prevent HPV-related diseases: the BEST study. Favato, G ; Baio, G ; Capone, A ; Marcellusi, A ; Costa, S ; Garganese, G ; Picardo, M ;. Favato, G ; Baio, G ; Capone, A ; Marcellusi, A ; Costa, S ; Garganese, G ; Picardo, M ; Drummond, M ; Jonsson, B ; Scambia, G ; Zweifel, P ; Mennini, FS ; - view fewer 2012. Novel health economic evaluation of a vaccination s...http://discovery.ucl.ac.uk/1358329/

Wells' Criteria for DVT - MDCalcessentially from

**Bayes****theorem**, that discordance between the ... essentially from**Bayes****theorem**, that discordance between the...http://mdcalc.com/wells-criteria-dvt/

Timeline of probability and statistics, 1761 – Thomas

**Bayes**proves**Bayes**'**theorem**, 1786 – Playfair ... Bernstein–von Mises**theorem**on the asymptotic irrelevance of prior ... 1654 – Pascal and Fermat create the mathematical theory of probability, 1657 – Huygens 's 'De ratiociniis in ludo aleae' is the first book on mathematical probability, 1662 – Graunt 's 'Natural and Political Observations Made upon the Bills of Mortality' makes inferences from statistical data on deaths in London, 1666 - In Le Journal des Sçavans xxxi, August 2, 1666 359-370 =364 appears a review of the third edition 1665 of John Graunt's Observations on the Bills of Mortality. 1710 – Arbuthnot argues that the constancy of the ratio of male to female births is a sign of divine providence, 1713 – Posthumous publication of Jacob Bernoulli 's ' Ars Conjectandi ', containing the first derivation of a law of large numbers, 1724 – Abraham de Moivre studies mortality statistics and the foundation of the theory of annuitie s in 'Annuities on Lives', 1733 – Abraham de Moivre introduc...https://en.wikipedia.org/wiki/Timeline_of_probability_and_statistics

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...http://mathhelpforum.com/advanced-statistics/90498-how-would-i-apply-bayes-theorem.html

Mass Spectrometry **Bayes**ian 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

**Bayes**ian Network Analysis Tool WMBAT The William and Mary**Bayes**ian Analysis Tool. View all files. 7 Downloads last 30 days File Size: 16.8 KB File ID: #24345 Version: 1.2 Mass Spectrometry**Bayes**ian 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**Bayes**ian 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**Bayes**ian Network from mass spectrometry data. The root node of the**Bayes**ian 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

**Bayes**ian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images (**Bayes**ian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images PDF Download Available. Article**Bayes**ian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images. Page 1**Bayes**ian 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**Bayes**ian 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: **Bayes**ian 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**Bayes**ian 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**Bayes**ian 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**Bayes**ian variable selection: Concepts and questions 325 11.4 Introduction to**Bayes**ian variable selection 326 11.5 Variable selection based on Zellner s g-prior 333 11.6 Variable selection based on Reversibl...http://wiley.com/WileyCDA/WileyTitle/productCd-1118314573.html

Further DNA segmentation analysis using approximate **Bayes**ian computation | NOVA. The University of NFurther DNA segmentation analysis using approximate

**Bayes**ian computation. The University of Newcastle's Digital Repository. The University of Newcastle's Digital Repository. List Of Titles Further DNA segmentation analysis using approximate**Bayes**ian computation. Title Further DNA segmentation analysis using approximate**Bayes**ian 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

**Bayes**ian 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...http://healthyalgorithms.com/2013/01/02/1933/

ORBi: Vandenplas Jérémie - **Bayes**ian integration of external information into the single step appro... ach for genomically enhanced prediction of breeding values. User guide. Legal guide. Reference :

**Bayes**ian integration of external information into the single step approach for genomi... Title :**Bayes**ian 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 :**Bayes**ian ; 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...http://appbrain.com/app/does-god-exist/borland.doesgodexist

The Infinite Monkey **Theorem**Petite Sirah is Vegan Friendly - Barnivore vegan wine guide... The Infinite Monkey

**Theorem**Petite Sirah is Vegan Friendly. http://theinfinitemonkey**theorem**.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...http://barnivore.com/products/15577-the-infinite-monkey-theorem-petite-sirah

**Bayes**ian Gene Expression... S

**Bayes**ian 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**Bayes**ian 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**Bayes**ian 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 ...http://realmagick.com/divergence-theorem-applications/

HyperparameterIn

**Bayes**ian 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

**Bayes**ian networks Thu 01-10-2015 For making probabilistic inferences, a graph is worth a thousand words. This month we continue with the theme of**Bayes**ian statistics and look at**Bayes**ian networks, which combine network analysis with**Bayes**ian statistics. 2015 Points of Significance:**Bayes**ian 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 **Bayes**ian 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

**Bayes**ian 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**Bayes**ian 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...http://orbi.ulg.ac.be/handle/2268/103440

Tutorial: Graphical Models and **Bayes**ian 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

**Bayes**ian 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**Bayes**ian 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**Bayes**ian network. Prerequisites Attendees are assumed to have a...http://people.math.aau.dk/~sorenh/misc/2014-useR-GMBN/

**Bayes**ian 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.

**Bayes**ian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor.**Bayes**ian 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...http://realmagick.com/hales-jewett-theorem/

**Bayes**ian 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

**Bayes**ian Missing Data Problems: EM, Data Augmentation and Noniterative Computati...http://onlinelibrary.wiley.com/doi/10.1111/j.1751-5823.2010.00122_32.x/citedby?globalMessage=0

mcmc | **bayes**ianbiologistmcmc.

**bayes**ianbiologist.**bayes**ianbiologist 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**Bayes**ian Methods guest lecture By Corey Chivers. Posted in Probability, Rstats, Teaching. Tagged**bayes**ian, 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**Bayes**ian 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**Bayes**ian Methods By Corey Chivers. Posted in Probability, Rstats, Teaching. Tagged**bayes**ian, 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...http://bayesianbiologist.com/tag/mcmc/

.. Monthly Archives: November 2012 .. The Density Cluster Tree: A Guest Post .. WHAT IS **BAYES**IAN/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

**BAYES**IAN/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**Bayes**ian inference is and what Frequentist inference is. Frequentist inference and**Bayes**ian 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**Bayes**ian Inference. And conversely, it is possible to do**Bayes**ian inference without using**Bayes**’**theorem**as Michael Goldstein, for example, has shown. As I will discuss in that review, Nate argues forcefully that**Bayes**ian analysis is superior to Frequentist anal...https://normaldeviate.wordpress.com/2012/11/

**Bayes**ian approaches to brain function... '

**Bayes**ian 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**Bayes**ian 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**Bayes**ian probability. N Rao Editor 2007,**Bayes**ian Brain: Probabilistic Approaches to Neural Coding, The MIT Press; 1 edition Jan 1 2007 Knill David,Pouget Alexandre 2004, The**Bayes**ian 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**Bayes**ian?" '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 **Bayes**ian 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 **Bayes**ian ClassifierDesigning Optimal Sequential ExperDesigning Optimal Sequential Experiments for a

**Bayes**ian Classifier. Designing Optimal Sequential Experiments for a**Bayes**ian 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**Bayes**ian 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

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

**bayes**ian 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.

**Bayes**i...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**Bayes**ian 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**Bayes**ian 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...https://dartthrowingchimp.wordpress.com/2012/12/31/dr-bayes-or-how-i-learned-to-stop-worrying-and-love-updating/?like=1&source=post_flair&_wpnonce=c589d5bcbb

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

**Bayes**ian 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**Bayes**ian 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

**bayes**ian 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 **Bayes**ian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistic... s. Constructing

**Bayes**ian 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**Bayes**ian 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**Bayes**ian 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 Likelihoods to Conditional Probabilities for **Bayes**ian Networks... c skaaning f jensen u kjaerulff and a madsen when developing real world applications of

**bayes**ian 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 likelihoods calling for methods to transform it into conditional probabilities suitable for the**bayes**ian 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

**Bayes**ian statistics with application to remote sensing: 2. J Geophys., 109, D10304, doi:10.1029/2003JD004174. Rossow, 2004: Neural network uncertainty assessment using**Bayes**ian statistics with application to remote sensing: 3. J Geophys., 109, D10305, doi:10.1029/2003JD004175. Rossow, 2004: Neural network uncertainty assessment using**Bayes**ian 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...http://pubs.giss.nasa.gov/year/2004.html

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.

**Bayes**ian 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**Bayes**ian Model Averaging in the M-Open Framework. In ``**Bayes**ian 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

**bayes**ian 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...http://math.stackexchange.com/questions/251833/extension-of-liouvilles-theorem

Réunion d'hiver SMC 2009HELENE MASSAM, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 A conjugate prior for discrete hierarchical loglinear models In

**Bayes**ian 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...http://cms.math.ca/Reunions/hiver09/abs/ms.html

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

**Bayes**ian 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.

**Bayes**ian Networks and the Problem of Unreliable Instruments. Bovens, Luc and Hartmann, Stephan 2000**Bayes**ian Networks and the Problem of Unreliable Instruments. Preview. PDF Download 2214Kb. Preview. Abstract We appeal to the theory of**Bayes**ian 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...http://math.stackexchange.com/questions/889241/a-consequence-of-wilsons-theorem

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...http://mathoverflow.net/questions/82277/shannon-mcmillan-breiman-theorem

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

**Bayes**ian Operational Modal Analysis... '

**Bayes**ian Operational Modal Analysis' BAYOMA adopts a**Bayes**ian 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**Bayes**ian 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

**Bayes**ian Analysis in the Health Sciences EPIB-683 Intermediate**Bayes**ian 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**Bayes**ian Analysis in Medicine EPIB-668 Introduction to**Bayes**ian Analysis in the Health Sciences EPIB-669 Intermediate**Bayes**ian Analysis for the Health Sciences EPIB-675**Bayes**ian Analysis in the Health Sciences Publications Methodological publications**Bayes**ian 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**Bayes**ian software**Bayes**ian Sample Size Change-point methods and applications Diagnostic testing Diagnostic testing in Genetics Links. Courses taught EPIB-682 Introduction to Bay...http://med.mcgill.ca/epidemiology/Joseph/SiteMap.html

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...http://r-bloggers.com/2013/10/page/2/

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....http://dead-dog-bounce.blogspot.com/

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 ::

**Bayes**ian Statistics :: Specific Model Fitting. Programming Language :: R. Programming Language :: Tcl. Operating System :: OS Independent. Natural Language :: English. Development Status :: 4 - Beta. Topic**Bayes**ian 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...https://r-forge.r-project.org/softwaremap/trove_list.php?form_cat=18&discrim=62,307,182,235,275,10

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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INI : Abstracts : SCBW03 : Exact **Bayes**ian inference and model selection for some infection modelsini abstracts scbw exact

**bayes**ian 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**bayes**ian 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 **theorem**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...https://en.wikipedia.org/wiki/Shockley–Ramo_theorem

Church–Rosser **theorem**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...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 ...https://terrytao.wordpress.com/tag/gowers-uniformity-norms/

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...http://math.stackexchange.com/questions/145338/is-there-a-simple-way-to-prove-bertrands-postulate-from-the-prime-number-theore

.. Post navigation .. Introduction to **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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=07995c002f

.. Post navigation .. Introduction to **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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 **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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 **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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 **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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 **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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 **Bayes**ian Methods guest lecture .. Share this: .. Like this:post navigation dark matter benchmarks all over the map introduction to

**bayes**ian lecture accompanying handouts and demos october introduction to**bayes**ian methods guest lecture by corey chivers posted in probability rstats teaching tagged**bayes**ian 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**bayes**ian 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=e5382928c8

Newton Institute Seminar : Pratola, M, 09/09/2011... The INI has a new website. DAE. Seminars. Pratola, M. DAE Seminar.

**Bayes**ian 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

**Bayes**ian vector autoregression... in statistics

**bayes**ian vector autoregression bvar uses**bayes**ian 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**bayes**ian 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****Bayes**ian 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 **theorem**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...https://en.wikipedia.org/wiki/Favard's_theorem

Tverberg's **theorem**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 ...https://en.wikipedia.org/wiki/Tverberg's_theorem

.. Post navigation .. Introduction to **Bayes**ian Methods guest lecture .. Share this: .. Like this:Post navigation. ← Dark matter benchmarks: All over the map. Introduction to

**Bayes**ian lecture: Accompanying handouts and demos →. October 18, 2012 Introduction to**Bayes**ian Methods guest lecture. By Corey Chivers ¶. Posted in Probability, Rstats, Teaching. ¶. Tagged**bayes**ian, 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**Bayes**ian 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=02ce0efcc9

.. Post navigation .. Introduction to **Bayes**ian Methods guest lecture .. Share this: .. Like this:Post navigation. ← Dark matter benchmarks: All over the map. Introduction to

**Bayes**ian lecture: Accompanying handouts and demos →. October 18, 2012 Introduction to**Bayes**ian Methods guest lecture. By Corey Chivers ¶. Posted in Probability, Rstats, Teaching. ¶. Tagged**bayes**ian, 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**Bayes**ian 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=ae4d6bb289

.. Post navigation .. Introduction to **Bayes**ian Methods guest lecture .. Share this: .. Like this:Post navigation. ← Dark matter benchmarks: All over the map. Introduction to

**Bayes**ian lecture: Accompanying handouts and demos →. October 18, 2012 Introduction to**Bayes**ian Methods guest lecture. By Corey Chivers ¶. Posted in Probability, Rstats, Teaching. ¶. Tagged**bayes**ian, 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**Bayes**ian 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=d763d6158f

Master **theorem**... In the analysis of algorithms, the 'master

**theorem**' provides a solution in asymptotic terms using Big O notation for recurrence relation s of types that occur in the analysis of many divide and conquer algorithm s. Not all recurrence relations can be solved with the use of the master**theorem**; its generalizations include the Akra–Bazzi method. : T n \in \Theta\left n {\log b a} \right. Next, we see if we satisfy the case 1 condition: : \log b a = \log 2 8 = 3>c. It follows from the first case of the master**theorem**that. : T n \in \Theta\left n {\log b a} \right = \Theta\left n {3} \right. : f n = \Theta\left n {c} \log {k} n \right where c = \log b a. : T n = \Theta\left n {c} \log {k+1} n \right. : a = 2, \, b = 2, \, c = 1, \, f n = 10n : f n = \Theta\left n {c} \log {k} n\right where c = 1, k = 0 Next, we see if we satisfy the case 2 condition: : \log b a = \log 2 2 = 1, and therefore, yes, c = \log b a. : T n = \Theta\left n {\log b a} \log {k+1} n\right = \Theta\left n {1} \log {1} n\right = \Theta\l...https://en.wikipedia.org/wiki/Master_theorem

Building Application of Pythagorean **Theorem**... USING THE PYTHAGOREAN

**THEOREM**IN CONSTRUCTION. Often, when builders want to lay the foundation for the corners of a building, one of the methods they use is based on the Pythagorean**Theorem**serious. In the previous pages we explored some special right triangles. One of them is the 3-4-5 triangle. Builders use this special triangle or a multiple of it, say, 9-12-15 when they don't have a carpenter's square an instrument for constructing right angles handy. You need a Java-enabled browser to view this applet. This is the process they follow :. First, they peg a string down where they want a specific wall to be. Then, they measure in feet usually a length of the string that is a multiple of three, say two times three, and mark that off so they would be marking off a section that was six feet long. Call the marked endpoints points A and B, where B is where the corner is to be built. Where they want the corner to be point B they attach another piece of string. If we are basing this method on the Pythagorean T...http://geom.uiuc.edu/~hipp/app2.html

Diophantine Approximation and Dirichlet Series... Return to List Item: 1 of 1. Diophantine Approximation and Dirichlet Series Herv Queff lec and Martine Queff lec, Universit de Lille 1, Villeneuve d'Ascq, France A publication of Hindustan Book Agency. Hindustan Book Agency 2013; 244 pp; softcover ISBN-13: 978-93-80250-53-3 List Price: US$52 Member Price: US$41.60 Order Code: HIN/63. It is devoted to Diophantine approximation, the analytic theory of Dirichlet series, and some connections between these two domains, which often occur through the Kronecker approximation

**theorem**. Accordingly, the book is divided into seven chapters, the first three of which present tools from commutative harmonic analysis, including a sharp form of the uncertainty principle, ergodic theory and Diophantine approximation to be used in the sequel. Chapters four and five present the general theory of Dirichlet series, with classes of examples connected to continued fractions, the famous Bohr point of view, and then the use of random Dirichlet series to produce non-trivial extrem...http://ams.org/cgi-bin/bookstore/booksearch?fn=100&pg1=CN&s1=Queffelec_Martine&arg9=Martine_Queff%E9lec

File:Posterior.pngfile posterior png file posterior png posterior probability distribution of the downstroke match rate for a

**bayes**ian analysis of the howland will forgery trial problem the image was created to show that the analysis of the problem by benjamin pierce makes the assumption that the match rate is exactly equal to in this**bayes**ian posterior shows that there is some uncertainty around this figure it is a plot of a beta distribution the image was created by myself blaise f egan blaise blaisefegan me uk using mathematica on th january then pasted into paint shop pro and saved as a png file...https://en.wikipedia.org/wiki/File:Posterior.png

.. **Bayes**ian courses in KÃ¸benhavn .. Share: .. Related« Compstat 2010. Short introduction to

**Bayes**ian analysis ».**Bayes**ian courses in KÃ¸benhavn I received this announcement about two incoming courses given in KÃ¸benhavn by Andrew Lawson:. 1 “*An Introduction to**Bayes**ian Disease Mapping*”. A Two-Day Course, April 12.- 13. 2010, University of Southern Denmark This course is designed to provide an introduction to the area of**Bayes**ian disease mapping in applications to Public Health and Epidemiology:. 2 “*Advanced**Bayes**ian Disease Mapping*”. A Two-Day Course, April 15. – 16. 2010, University of Southern Denmark, Copenhagen, Denmark This course is designed to provide advanced coverage of**Bayes**ian disease mapping topics in applications to Public Health and Epidemiology: It is intended as an extension to the course: *An Introduction to**Bayes**ian Disease Mapping*. Emphasis on the course is placed on spatial and spatio-temporal**Bayes**ian modeling issues, and some knowledge of**Bayes**ian computation and WinBUGS is assumed. Share:. Share. Click to email this to a friend Opens...https://xianblog.wordpress.com/2010/01/27/bayesian-courses-in-københavn/

IS Home Page... EPIB 607 Principles of Inferential Statistics in Medicine. Prerequisite: Basic understanding of differentiable and integral calculus. Enrollment in the Epidemiology program at McGill University. Objectives:. The aim of this course is to provide students with basic principles of statistical inference applicable to clinical and epidemiologic so that they can: i understand how statistical methods are used by others, ii apply statistical methods in their own research, and iii use the methods learned in this course as a foundation for more advanced biostatistics courses. Content: Topic:. Baldi Moore:. Data collection, description, and display. Chapters 1, 2. . Probability, Discrete and continuous distributions Randomness and random variables

**Bayes****Theorem**Diagnostic tests. . Chapters 9 - 12. Inference for means Sampling distributions Inference and estimating with confidence Hypothesis testing Paired and unpaired data Sample size and power. . Chapters 13 - 18. Inference for proportions Paired and unpaired data...http://med.mcgill.ca/epidemiology/moodie/InferentialStats.htm

**bayes**py/**bayes**py · GitHub... Skip to content. Sign up Sign in. This repository. Explore. Features. Enterprise. Pricing. Watch. 32. Star. 177. Fork. 37.

**bayes**py. /**bayes**py. Code Issues. Pull requests. Pulse Graphs HTTPS clone URL. Subversion checkout URL. You can clone with. . HTTPS or. . Subversion. Download ZIP.**Bayes**ian Python:**Bayes**ian inference tools for Python. http://**bayes**py.org. 1,002 commits. 4 branches. 13 releases. Fetching contributors Python 100.0%. Python. Branch: master. Switch branches/tags. Branches. Tags. develop. gh-pages. master. travis-versions. Nothing to show. 0.3.7. 0.3.6. 0.3.5. 0.3.4. 0.3.3. 0.3.2. 0.3.1. 0.3. 0.2.3. 0.2.2. 0.2.1. 0.2. 0.1. Nothing to show.**bayes**py. / REL: Version 0.3.7. latest commit 1fcb583874. jluttine authored Sep 23, 2015. Permalink. Failed to load latest commit information.**bayes**py. ENH: Add initial support for logging. Sep 23, 2015. doc. DOC: Enable doc makefile to use Sphinx outside virtualenv. Sep 21, 2015. .coveragerc. Fix Travis CI installation and coverage sections. Apr 22, 2014....https://github.com/bayespy/bayespy

Chebotaryov **theorem**on roots of unity... the

**theorem**state that all submatrices of a dft matrix of prime length are invertible the chebotaryov**theorem**on roots of unity was originally a conjecture made by ostrowski in the context of lacunary series chebotaryov was the first to prove it in the s this proof involves tools from galois theory and did not please ostrowski who made comments arguing that it does not meet the requirements of mathematical esthetics stevenhagen et al several proofs have been proposed since p e frenkel and it has even been discovered independently by dieudonné j dieudonné statement applications notes references statement let omega be a matrix with entries a ij omega ij leq i j leq n where omega e i pi n n in mathbb n if n is prime then any minor of omega is non zero applications for signal processing purposes candès romberg tao as a consequence of the chebotaryov**theorem**on roots of unity t tao stated an extension of the uncertainty principle t tao notes references category**theorem**s in linear algebra category**theorem**s in ...https://en.wikipedia.org/wiki/Chebotaryov_theorem_on_roots_of_unity

Cut-elimination **theorem**... It was originally proved by Gerhard Gentzen 1934 in his landmark paper "Investigations in Logical Deduction" for the systems LJ and LK formalising intuitionistic and classical logic respectively. The cut-elimination

**theorem**states that any judgement that possesses a proof in the sequent calculus that makes use of the 'cut rule' also possesses a 'cut-free proof', that is, a proof that does not make use of the cut rule., gives a 5-page proof of the elimination**theorem**., gives a very brief proof of the cut-elimination**theorem**. The LHS may have arbitrarily many or few formulae; when the LHS is empty, the RHS is a tautology. In LK, the RHS may also have any number of formulae—if it has none, the LHS is a contradiction, whereas in LJ the RHS may only have one formula or none: here we see that allowing more than one formula in the RHS is equivalent, in the presence of the right contraction rule, to the admissibility of the law of the excluded middle. However, the sequent calculus is a fairly expressive framewor...https://en.wikipedia.org/wiki/Cut-elimination_theorem

Chou's invariance **theorem**... Where a distance that would, in standard statistical theory, be defined as a Mahalanobis distance cannot be defined in this way because the relevant covariance matrix is singular, a replacement would be to reduce the dimension of the multivariate space until the relevant covariance matrix is invertible. Chou's invariance

**theorem**says that it does not matter which of the coordinates are selected for removal, as the same values of distance would be calculated as a final result. Background Essence Proof Applications References. When using Mahalanobis distance or covariant discriminant to calculate the similarity of two proteins based on their amino acid compositions, to avoid the divergence problem due to the normalization condition imposed to their 20 constituent components, a dimension-reduced operation is needed by leaving out one of the 20 components and making the remaining 19 components completely independent. Generally speaking, to calculate the Mahalanobis distance or covariant discriminant between ...https://en.wikipedia.org/wiki/Chou's_invariance_theorem

Normalized random measures driven by increasing additive processes - Kent Academic Repositorynormalized random measures driven by increasing additive processes kent academic repository login admin simple search advanced search home browse latest additions help contact normalized random measures driven by increasing additive processes nieto barajas luis e and prunster igor and walker stephen g normalized random measures driven by increasing additive processes annals of statistics pp issn full text available pdf normalized random measures download kb preview official url http dx doi org abstract this paper introduces and studies a new class of nonparametric prior distributions random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes in particular we present results for the distribution of means under both prior and posterior conditions and via the use of strategic latent variables undertake a ful...

https://kar.kent.ac.uk/10537/

Items where Author is "Carlin, Brad" - Messanae Universitas Studiorumitems where author is carlin brad messanae universitas studiorum messanae universitas studiorum home about browse by year browse by subject browse by author login create account items where author is carlin brad up a level export as ascii citation bibtex dublin core ep xml endnote html citation json mets oai ore resource map atom format oai ore resource map rdf format object ids openurl contextobject rdf n triples rdf n rdf xml refer reference manager rss rss atom group by item type no grouping jump to conference or workshop item number of items conference or workshop item banerjee sudipto and jin xiaoping and carlin brad hierarchical

**bayes**ian models and their implementation in multivariate disease mapping in s i s statistica e ambiente settembre messina italy this list was generated on mon oct cest messanae universitas studiorum is powered by eprints which is developed by the school of electronics and computer science at the university of southampton more information and software credits...http://cab.unime.it/mus/view/creators/Carlin=3ABrad=3A=3A.html

Bernstein's **theorem**(approximation theory)bernstein s

**theorem**approximation theory bernstein s**theorem**approximation theory in approximation theory bernstein s**theorem**is a converse to jackson s**theorem**the first results of this type were proved by sergei bernstein in for approximation by trigonometric polynomials the result is as follows let f c be a π periodic function and assume r is a natural number and α if there exists a number c f and a sequence of trigonometric polynomial s p n n n such that deg p n n quad sup leq x leq pi f x p n x leq frac c f n r alpha then f p n φ where φ has a bounded r th derivative which is α hölder continuous see also bernstein s lethargy**theorem**constructive function theory references category**theorem**s in approximation theory...https://en.wikipedia.org/wiki/Bernstein's_theorem_(approximation_theory)

Regularity **theorem**for Lebesgue measure... in mathematics the regularity

**theorem**for lebesgue measure is a result in measure theory that states that lebesgue measure on the real line is a regular measure informally speaking this means that every lebesgue measurable subset of the real line is approximately open and approximately closed statement of the**theorem**lebesgue measure on the real line r is a regular measure that is for all lebesgue measurable subsets a of r and ε there exist subsets c and u of r such that c is closed and u is open and c a u and the lebesgue measure of u c is strictly less than ε moreover if a has finite lebesgue measure then c can be chosen to be compact i e by the heine borel**theorem**closed and bounded corollary the structure of lebesgue measurable sets if a is a lebesgue measurable subset of r then there exists a borel set b and a null set n such that a is the symmetric difference of b and n a b triangle n left b setminus n right cup left n setminus b right see also radon measure category**theorem**s in measure theory...https://en.wikipedia.org/wiki/Regularity_theorem_for_Lebesgue_measure

Nyquist's **Theorem**- Everything2.com... Nyquist's

**Theorem**. Nyquist 's**theorem**is one of the most fundamental in digital signal processing. It says that when sampling a signal, the sample rate needs to be at least twice the highest frequency which appears in the signal. Sampling at this rate ensures that the signal can be reconstruct ed perfectly from the digital samples. It is quite easy to prove the sampling**theorem**, or at least to make it plausible, using the convolution**theorem**. Now sampling a function in time or space or whatever means to multiply it by a comb function. Thus we could just as well convolute the spectrum of the function in question which is its Fourier transform with the Fourier transform of the comb. The comb is essentially a collection of delta functions arranged in a regular grid. Delta functions are very easy to convolute: Just imagine you put a copy of the other function around every delta peak. And here we are: Obviously, if we want to retrieve our sampled function perfectly, the different copies of the spectrum may no...http://everything2.com/title/Nyquist%27s Theorem

Lusin's **theorem**lusin s

**theorem**lusin s**theorem**in the mathematical field of real analysis lusin s**theorem**or luzin s**theorem**named for nikolai luzin states that every measurable function is a continuous function on nearly all its domain in the informal formulation of j e littlewood every measurable function is nearly continuous classical statement for an interval let f rightarrow mathbb c be a measurable function then for every ε there exists a compact e such that f restricted to e is continuous and mu e b a varepsilon note that e inherits the subspace topology from continuity of f restricted to e is defined using this topology general form let x sigma mu be a radon measure space and y be a second countable topological space let f x rightarrow y be a measurable function given ε for every a in sigma of finite measure there is a closed set e with µ a e ε such that f restricted to e is continuous if a is locally compact we can choose e to be compact and even find a continuous function f varepsilon x rightarrow y with compact s...https://en.wikipedia.org/wiki/Lusin's_theorem

Minlos' **theorem**minlos

**theorem**minlos**theorem**in the mathematics of topological vector space s minlos**theorem**states that a cylindrical measure on the dual of a nuclear space is a radon measure if its fourier transform is continuous it is named after robert adol fovich minlos and can be proved using sazonov s**theorem**references category**theorem**s in functional analysis category probability**theorem**s category stochastic processes...https://en.wikipedia.org/wiki/Minlos'_theorem

Nagata's compactification **theorem**nagata s compactification

**theorem**nagata s compactification**theorem**in algebraic geometry nagata s compactification**theorem**introduced by implies that every abstract variety can be embedded in a complete variety and more generally shows that a separated and finite type morphism to a noetherian scheme s can be factored into an open immersion followed by a proper mapping deligne showed in unpublished notes expounded by conrad that the condition that s is noetherian can be replaced by the condition that s is quasi compact and quasi separated nagata s original proof used the older terminology of zariski riemann space s and valuation theory which sometimes made it hard to follow gave a scheme theoretic proof of nagata s**theorem**nagata s**theorem**is used to define the analogue in algebraic geometry of cohomology with compact support or more generally higher direct image functors with proper support references category**theorem**s in algebraic geometry...https://en.wikipedia.org/wiki/Nagata's_compactification_theorem

1132: Frequentists vs. **Bayes**ians - explain xkcd**Bayes**ians focus on conditional probability - the likelihood that one event is true if you are given that some other related event is true. + :Frequentist Statistician: This neutrino detector measures whether the sun has gone nova. Has the sun gone nova. + :Frequentist Statistician: The probability of this result happening by chance is 1/36=0.027. + :**Bayes**ian Statistician: Bet you $50 it hasn't. It's night, so we're not sure Frequentist Statistician: This neutrino detector measures whether the sun has gone nova. Frequentist Statistician: Frequentist Statistician: The probability of this result happening by chance is 1/36=0.027. Let event N be the sun going nova and event Y be the detector giving the answer "Yes". My personal interpretation of the "bet you $50 it hasn't" reply is in the case of the sun going nova, no one would be alive to ask the neutrino detector, the probability of the sun going nova is always 0. Therefore, there is a 35/36 probability 97.22% that the machine is telling the truth and therefor...http://explainxkcd.com/wiki/index.php?title=1132:_Frequentists_vs._Bayesians&diff=20369&oldid=16671

Strolen's Citadel: The Questing Beasts By Scrasamax... I am Exploring. New Submissions. 62 xp. Comments: 11. Ideas: 5. Submitted: October 16, 2006, 12:53 am Updated: October 16, 2006, 2:52 pm. Vote Hall of Honour Cheka Man manfred valadaar. The Questing Beasts By: Scrasamax T would take 30 hounds and a brace of knights to hunt a questing beast. These are the Questing Beasts. A Questing Beast is a rare and unique creature that has very obvious magical traits and innate magical powers. If drawn into combat Questing Beasts take half damage from mundane weapons and normal damage from magical weapons, though their magical powers have a 50% chance of not affecting the beast. Thusly a beast struck with a Flaming Sword will take the normal damage from the sword, but only a 50% chance of taking the fire/flame damage. Common Abilities All Questing Beasts are innately magical creatures and all of them have a supply of spells and spell like powers to call upon as needed. Additional Ideas 5 The Rune Beast Also known as the Tyursha TIE-yur-SHA the Rune beast is slightly l...

http://strolen.com/viewing/The_Questing_Beasts

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**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.

**P-adic Hodge theory**: In mathematics, p-adic Hodge theory is a theory that provides a way to classify and study p-adic Galois representations of characteristic 0 local fieldsIn this article, a local field is complete discrete valuation field whose residue field is perfect. with residual characteristic p (such as Qp).

**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.

**Bill Parry (mathematician)**

**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.

**Index of information theory articles**: This is a list of information theory topics, by Wikipedia page.

**Von Neumann regular ring**: In mathematics, a von Neumann regular ring is a ring R such that for every a in R there exists an x in R such that . To avoid the possible confusion with the regular rings and regular local rings of commutative algebra (which are unrelated notions), von Neumann regular rings are also called absolutely flat rings, because these rings are characterized by the fact that every left module is flat.

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

**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.

**Enzyme Commission number**: The Enzyme Commission number (EC number) is a numerical classification scheme for enzymes, based on the chemical reactions they catalyze.

**Yarrow oil**

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

**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.

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**Bayesian inference on biopolymer models.**

MOTIVATION: Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics. RESULTS: The assignment of probabilities for all possible values of all unknown variables in a problem in the form of a posterior distribution is the goal of Bayesian inference. Here we show how this goal can be achieved for most bioinformatics methods that use dynamic programming. Specifically, a tutorial style description of a Bayesian inference procedure for segmentation of a sequence based on the heterogeneity in its composition is given. In addition, full Bayesian inference algorithms for sequence alignment are described. AVAILABILITY: Software and a set of transparencies for a tutorial describing these ideas are available at http://www.wadsworth.org/res&res/bioinfo/ (+info)

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**Genetic determination of individual birth weight and its association with sow productivity traits using Bayesian analyses.**

Genetic association between individual birth weight (IBW) and litter birth weight (LBW) was analyzed on records of 14,950 individual pigs born alive between 1988 and 1994 at the pig breeding farm of the University of Kiel. Dams were from three purebred lines (German Landrace, German Edelschwein, and Large White) and their crosses. Phenotypically, preweaning mortality of pigs decreased substantially from 40% for pigs with < or = 1 kg weight to less than 7% for pigs with > 1.6 kg. For these low to high birth weight categories, preweaning growth (d 21 of age) and early postweaning growth (weaning to 25 kg) increased by more than 28 and 8% per day, respectively. Bayesian analysis was performed based on direct-maternal effects models for IBW and multiple-trait direct effects models for number of pigs born in total (NOBT) and alive (NOBA) and LBW. Bayesian posterior means for direct and maternal heritability and litter proportion of variance in IBW were .09, .26, and .18, respectively. After adjustment for NOBT, these changed to .08, .22, and .09, respectively. Adjustment for NOBT reduced the direct and maternal genetic correlation from -.41 to -.22. For these direct-maternal correlations, the 95% highest posterior density intervals were -.75 to -.07, and -.58 to .17 before and after adjustment for NOBT. Adjustment for NOBT was found to be necessary to obtain unbiased estimates of genetic effects for IBW. The relationship between IBW and NOBT, and thus the adjustment, was linear with a decrease in IBW of 44 g per additionally born pig. For litter traits, direct heritabilities were .10, .08, and .08 for NOBT, NOBA, and LBW, respectively. After adjustment of LBW for NOBA the heritability changed to .43. Expected variance components for LBW derived from estimates of IBW revealed that genetic and environmental covariances between full-sibs and variation in litter size resulted in the large deviation of maternal heritability for IBW and its equivalent estimate for LBW. These covariances among full-sibs could not be estimated if only LBW were recorded. Therefore, selection for increased IBW is recommended, with the opportunity to improve both direct and maternal genetic effects of birth weight of pigs and, thus, their vitality and pre- and postnatal growth. (+info)

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**Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data.**

A general fine-scale Bayesian quantitative trait locus (QTL) mapping method for outcrossing species is presented. It is suitable for an analysis of complete and incomplete data from experimental designs of F2 families or backcrosses. The amount of genotyping of parents and grandparents is optional, as well as the assumption that the QTL alleles in the crossed lines are fixed. Grandparental origin indicators are used, but without forgetting the original genotype or allelic origin information. The method treats the number of QTL in the analyzed chromosome as a random variable and allows some QTL effects from other chromosomes to be taken into account in a composite interval mapping manner. A block-update of ordered genotypes (haplotypes) of the whole family is sampled once in each marker locus during every round of the Markov Chain Monte Carlo algorithm used in the numerical estimation. As a byproduct, the method gives the posterior distributions for linkage phases in the family and therefore it can also be used as a haplotyping algorithm. The Bayesian method is tested and compared with two frequentist methods using simulated data sets, considering two different parental crosses and three different levels of available parental information. The method is implemented as a software package and is freely available under the name Multimapper/outbred at URL http://www.rni.helsinki.fi/mjs/. (+info)

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**The validation of interviews for estimating morbidity.**

Health interview surveys have been widely used to measure morbidity in developing countries, particularly for infectious diseases. Structured questionnaires using algorithms which derive sign/symptom-based diagnoses seem to be the most reliable but there have been few studies to validate them. The purpose of validation is to evaluate the sensitivity and specificity of brief algorithms (combinations of signs/symptoms) which can then be used for the rapid assessment of community health problems. Validation requires a comparison with an external standard such as physician or serological diagnoses. There are several potential pitfalls in assessing validity, such as selection bias, differences in populations and the pattern of diseases in study populations compared to the community. Validation studies conducted in the community may overcome bias caused by case selection. Health centre derived estimates can be adjusted and applied to the community with caution. Further study is needed to validate algorithms for important diseases in different cultural settings. Community-based studies need to be conducted, and the utility of derived algorithms for tracking disease frequency explored further. (+info)

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**Bayesian analysis of birth weight and litter size in Baluchi sheep using Gibbs sampling.**

Variance and covariance components for birth weight (BWT), as a lamb trait, and litter size measured on ewes in the first, second, and third parities (LS1 through LS3) were estimated using a Bayesian application of the Gibbs sampler. Data came from Baluchi sheep born between 1966 and 1989 at the Abbasabad sheep breeding station, located northeast of Mashhad, Iran. There were 10,406 records of BWT recorded for all ewe lambs and for ram lambs that later became sires or maternal grandsires. All lambs that later became dams had records of LS1 through LS3. Separate bivariate analyses were done for each combination of BWT and one of the three variables LS1 through LS3. The Gibbs sampler with data augmentation was used to draw samples from the marginal posterior distribution for sire, maternal grandsire, and residual variances and the covariance between the sire and maternal grandsire for BWT, variances for the sire and residual variances for the litter size traits, and the covariances between sire effects for different trait combinations, sire and maternal grandsire effects for different combinations of BWT and LS1 through LS3, and the residual covariations between traits. Although most of the densities of estimates were slightly skewed, they seemed to fit the normal distribution well, because the mean, mode, and median were similar. Direct and maternal heritabilities for BWT were relatively high with marginal posterior modes of .14 and .13, respectively. The average of the three direct-maternal genetic correlation estimates for BWT was low, .10, but had a high standard deviation. Heritability increased from LS1 to LS3 and was relatively high, .29 to .37. Direct genetic correlations between BWT and LS1 and between BWT and LS3 were negative, -.32 and -.43, respectively. Otherwise, the same correlation between BWT and LS2 was positive and low, .06. Genetic correlations between maternal effects for BWT and direct effects for LS1 through LS3 were all highly negative and consistent for all parities, circa -.75. Environmental correlations between BWT and LS1 through LS3 were relatively low and ranged from .18 to .29 and had high standard errors. (+info)

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**Thermodynamics and kinetics of a folded-folded' transition at valine-9 of a GCN4-like leucine zipper.**

Spin inversion transfer (SIT) NMR experiments are reported probing the thermodynamics and kinetics of interconversion of two folded forms of a GCN4-like leucine zipper near room temperature. The peptide is 13Calpha-labeled at position V9(a) and results are compared with prior findings for position L13(e). The SIT data are interpreted via a Bayesian analysis, yielding local values of T1a, T1b, kab, kba, and Keq as functions of temperature for the transition FaV9 right arrow over left arrow FbV9 between locally folded dimeric forms. Equilibrium constants, determined from relative spin counts at spin equilibrium, agree well with the ratios kab/kba from the dynamic SIT experiments. Thermodynamic and kinetic parameters are similar for V9(a) and L13(e), but not the same, confirming that the molecular conformational population is not two-state. The energetic parameters determined for both sites are examined, yielding conclusions that apply to both and are robust to uncertainties in the preexponential factor (kT/h) of the Eyring equation. These conclusions are 1) the activation free energy is substantial, requiring a sparsely populated transition state; 2) the transition state's enthalpy far exceeds that of either Fa or Fb; 3) the transition state's entropy far exceeds that of Fa, but is comparable to that of Fb; 4) "Arrhenius kinetics" characterize the temperature dependence of both kab and kba, indicating that the temperatures of slow interconversion are not below that of the glass transition. Any postulated free energy surface for these coiled coils must satisfy these constraints. (+info)

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**Iterative reconstruction based on median root prior in quantification of myocardial blood flow and oxygen metabolism.**

The aim of this study was to compare reproducibility and accuracy of two reconstruction methods in quantification of myocardial blood flow and oxygen metabolism with 15O-labeled tracers and PET. A new iterative Bayesian reconstruction method based on median root prior (MRP) was compared with filtered backprojection (FBP) reconstruction method, which is traditionally used for image reconstruction in PET studies. METHODS: Regional myocardial blood flow (rMBF), oxygen extraction fraction (rOEF) and myocardial metabolic rate of oxygen consumption (rMMRO2) were quantified from images reconstructed in 27 subjects using both MRP and FBP methods. For each subject, regions of interest (ROIs) were drawn on the lateral, anterior and septal regions on four planes. To test reproducibility, the ROI drawing procedure was repeated. By using two sets of ROIs, variability was evaluated from images reconstructed with the MRP and the FBP methods. RESULTS: Correlation coefficients of mean values of rMBF, rOEF and rMMRO2 were significantly higher in the images reconstructed with the MRP reconstruction method compared with the images reconstructed with the FBP method (rMBF: MRP r = 0.896 versus FBP r = 0.737, P < 0.001; rOEF: 0.915 versus 0.855, P < 0.001; rMMRO2: 0.954 versus 0.885, P < 0.001). Coefficient of variation for each parameter was significantly lower in MRP images than in FBP images (rMBF: MRP 23.5% +/- 11.3% versus FBP 30.1% +/- 14.7%, P < 0.001; rOEF: 21.0% +/- 11.1% versus 32.1% +/- 19.8%, P < 0.001; rMMRO2: 23.1% +/- 13.2% versus 30.3% +/- 19.1%, P < 0.001). CONCLUSION: The MRP reconstruction method provides higher reproducibility and lower variability in the quantitative myocardial parameters when compared with the FBP method. This study shows that the new MRP reconstruction method improves accuracy and stability of clinical quantification of myocardial blood flow and oxygen metabolism with 15O and PET. (+info)

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**Taking account of between-patient variability when modeling decline in Alzheimer's disease.**

The pattern of deterioration in patients with Alzheimer's disease is highly variable within a given population. With recent speculation that the apolipoprotein E allele may influence rate of decline and claims that certain drugs may slow the course of the disease, there is a compelling need for sound statistical methodology to address these questions. Current statistical methods for describing decline do not adequately take into account between-patient variability and possible floor and/or ceiling effects in the scale measuring decline, and they fail to allow for uncertainty in disease onset. In this paper, the authors analyze longitudinal Mini-Mental State Examination scores from two groups of Alzheimer's disease subjects from Palo Alto, California, and Minneapolis, Minnesota, in 1981-1993 and 1986-1988, respectively. A Bayesian hierarchical model is introduced as an elegant means of simultaneously overcoming all of the difficulties referred to above. (+info)

### What are some interactive ways to present the Pythagroas Theorem?

We want a way to interact with our class while presenting the Pythagoras Theorem. Sorry it's in the wrong category!

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Have them make a right triangle with 3 pieces of different color yarn one being like 5in long for the hypot. and 4 for the length and 3 for the height. Then show them the formula explain it and tell them to calculate the hypot.. with the formula and then show them by they were right by measuring the string.

Or if you don't want to mess with string just draw and example on an over head using like 3cm 4cm and 5cm with different color marker and then turn it on with two sides labeled and have them work out the solution walk around check everyones and then show them they were right or correct them.

### What is your opinion on the optimum amount and thickness of cottage cheese in a green salad with croutons?

Keep in mind the area and volume of the cottage cheese and the shape of the portions for triangles don't forget Pythagoras' Theorem. good luck soldier.

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I would not put cottage cheese in a green salad, croutons or no. But if I was told to make salads with it in a restaurant, I would use a 1/3 cup scoop to make a pretty mound of the cheese. ∠°)

### Where can I buy the candy Everlasting Gobstoppers?

I want to buy a bunch of packets for my math class (I'm doing a project on a four-color theorem)

And how much would it cost?

i don't think buying them online is a good idea... what if they are old and melted? And wouldn't you have to pay shipping?

u think they'd have them at Sam's Club?

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Here in the area I live you can buy them at most stores. I have seen them at WalMart, CVS, Rite Aid, KMart, Drug stores, Walgreens, Gas Stations, Dollar General, and I just discovered them at the Dollar Tree.

If you buy them at the Dollar Tree you will pay only a dollar. Other places the prices are slightly higher. I have seen the prices as high as $1.49 a box.

The best price I found is on the net at this website...

http://www.candywarehouse.com/gobstoppers.html

24 boxes for $19.20.

I have found them in theater size boxes only.

### What is the best technique used to remembering misc things?

Or your best technique? Like reading, names, basic vocabulary. Aside from from the obvious.

The reason I ask is because I have been through some times that were difficult, and I believe that I taught myself to block out difficult things, but I think I have trained my memory to do this with everything. It makes life very difficult, especially when I communicate.

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The best thing is note it down on your diary if it is to be remembered. If it is a reminder of your work after completing your work put a tick mark on it with the reference what action you have taken on it. Revise very often when ever you feel free to update and finish your pending works.This is how I survive. I will forget what I ate in the morning. But I am 62 years old still I remember Newtons law and Pythagoras theorem. It is all the matter of our interest.

### How do you make a sandwhich?

Its really hard to remember all of the steps.

That is too complicated! Pretend your talking to a 2nd grader.

Why do I need this for homework, I've been hungry for the past 2 hours trying to figure out the mechanics of making a sandwhich.

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This can't be taught to a second grader. It takes a much higher level of understanding to get it right.

It's not possible until you first learn and fully comprehend the Ham Sandwich Theorem. When you finish making the object and you cut it, no matter how you cut it, you can always slice through it in way that the two halves have exactly equal amounts.

So, taking some bread, ham and cheese and you mix them up by placing some ham and cheese between 2 pieces of the bread you will have 3 fixed volumes. Then with some kind of plane, for example a slice of a knife, you bisect with this plane each, the ham, bread, and cheese so you can apply the theroem. This is the test to see if you actually succeeded in making a sandwich. You will have two pieces of the once whole piece but they probably won't be perfectly equal in size. Not to worry. Really all the stuff was cut exactly in half.

We can see this when we apply the Borsuk-Ulam theorem. n-dimensional space in which there are n globs of positive volume, there is always a hyperplane that cuts all the globs exactly in half.

So, once you apply the theorem in the final test for example by taking the 3 objects, bread, ham and cheese compressing them together in a certain order then put them into a blender, pulsing it a few times, after all those millions of slices, all the parts will be cut exactly in half. You then know you will have achieved success.

Now you try it. You can do it!

Take 2 pieces of bread. Find 2 other food objects that do not need further cooking and are sort of flatly shaped and put them between the bread. Compress gently.

### How to make a Star of David with 24 cupcakes?

I need to make a Star of David out of two triangles, made out of cupcakes. It's for a bar mitzvah. I've arranged different ways over and over, but nothing is right! Please help!!

Thanks!

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The Star of David is made with two triangles. ...this pic is a good one to go by because it shows the triangles as two different colors and makes it easier to see.

http://threesixty360.wordpress.com/2008/12/21/star-of-david-theorem/

To make your cake you will want to make the first triangle...using 6 cupcakes for each side (the point cupcakes will be counted twice so you will actually be using 15 cupcakes total for the first triangle.

After you have made it you will make the second triangle by placing a cupcake, on the outside of the triangle...place a cupcake between cupcake 3 and 4....of each side of the triangle. (leave enough room for another cupcake between.) This will now give you your six points. Now, go to the OUTSIDE of the triangle and put two cupcakes ...lining them up with the 3rd cupcake down on each side of your original first triangle......you then fit that "point" cupcake up next to them...you should then have a row of 6 cupcakes. For each of the other two points you will do the same thing.....count over to the 3rd cupcake from the point and place a cupcake on the "outside" of the triangle. This should finish the star. You should have used a total of 24 cupcakes.

If you ice all of the cupcakes white...then draw the line for the star, it will show up a lot plainer as a star...otherwise it tends to look like a bunch of cupcakes, gone wild.

I work in a supermarket bakery and have made these several times. It does work.

I will try to find a finished picture of one.

### What food are good for encouraging spleen transformation in TCM?

Can anyone please tell me what things -foods primarily but herbs too are good for promoting normal function of spleen in Chinese Medicine so that it transforms foods and liquids normally/effectively?

Thankyou

Thanks Charity-been there done that,not quite what i'm looking for but good luck with sales anyway

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SPLEEN PAIN, DISEASES, Infection, ENLARGED SPLEEN

Hypersplenism/ Enlarged spleen

Hypersplenism is a type of disorder which causes the spleen to rapidly and prematurely destroy blood cells.

Causes & symptoms

Hypersplenism may be caused by a variety of disorders. Sometimes, it is brought on by a problem within the spleen itself and is referred to as primary hypersplenism. Secondary hypersplenism results from another disease such as chronic malaria, rheumatoid arthritis, tuberculosis, or polycythemia vera, a blood disorder.

Symptoms of hypersplenism include easy bruising, easy contracting of bacterial diseases, fever, weakness, heart palpitations, and ulcerations of the mouth, legs and feet. Individuals may also bleed unexpectedly and heavily from the nose or other mucous membranes, and from the gastrointestinal or urinary tracts. Most patients will develop an enlarged spleen, anemia, leukopenia, or abnormally low white blood cell counts, or thrombocytopenia, a deficiency of circulating platelets in the blood. Other symptoms may be presents that reflect the underlying disease that has caused hypersplenism.

An enlarged spleen is one of the symptoms of Malaria, Cirrhosis of the liver, leukaemia, lymphoma, Hodgkin's disease, polycythaemia, etc. Spleen enlarges when called on to remove massive numbers of red blood cells, defective cells, or bacteria from circulation. Splenomegaly occurs in about 10% of systemic lupus erythematosus patients. Sometimes, it is caused by recent viral infection, such as mononucleosis.

Homeopathic Medicines & Treatment for Pain in Spleen, Infection &, Enlarged Spleen :-

#Ceanothus [Cean]

The only sphere of action of this remedy seems to be in splenic troubles, and Burnett believes it to be a true organ remedy for the spleen. Its indications are deepseated pain in the splenic region, deep stitches, worse in damp weather, with enlargement of the spleen. Chronic pains in the spleen. Pain in whole left side, with shortness of breath. A splenic stitch usually requires one of the following remedies: Chelidonium, Berberis, Sulphur,Conium or Ceanothus. Scilla has pain in the left hypochondria region, and also in the epigastric region, relieved by lying on the right side. Cimicifuga. Bayes recommends this remedy in neuralgic pains in the splenic region with uterine complaints. Ranunculus bulbosus. Boenninghausen and Dunham considered this remedy of value in splenic troubles; soreness, stitches and pulsations in splenic region are present.

#Cinchona

This remedy corresponds to congestion, pain and stitches in the region of the spleen with swelling of the spleen, splenitis. Dull aching in region of spleen. Hyperaemia of spleen. Nervous system is sensitive, physical or mental effort aggravates. Chininum sulphuricum. Congestion, inflammation and enlargement of the spleen. Aranea diadema. Enlarged spleen. Especially useful for the chronic effects of malarial poisoning or in those who live in damp, wet places. Languor, lassitude, constant chilliness are useful symptoms. Grindelia robusta has pain in the splenic region; it has also enlargement and tenderness in this region. It seems to be applicable to any pain in the left side extending as low as the hip and as high as the nipple. It may be a sore aching or a keen cutting pain.

#Capsicum [Caps]

One of our most efficient remedies for sensitive swollen and enlarged spleens, according to Jahr. Arnica. Splenitis from injury, patient dull and apathetic. There is much testimony in favor of Arnica, especially where there is a typhoid tendency and dull or even acute pains. Bellis, which causes swelling in splenic region, Natrum muriaticum and Ferrum metallicum should also be thought of in enlarged spleen. Natrum muriaticum produces stitches, pressure and congestion in the spleen. Swollen spleens resulting from malarial fever. Patient anaemic, upper part of body emaciated, inclined to take cold; much quinine taken is an additional indication. Patient craves salt.

#Quercus

Enlarged spleen associated with alcoholic cirrhosis of liver, ankles swollen.

#Natrum mur

Swollen spleen accompanied by constipation and craving for salt, especially if person is oversensitive and gets even more upset when consolation is offered.

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Take the remedy which is similar to your symptoms. No side effects or complications if taken as directed, please do not exceed the given dosage and under any circumstances do not try to mix any remedies and avoid Chocolates, Mints, Coffee, Red Meat, Alcoholic and Carbonated drinks, Spicy Rich Food while taking any Homeopathic remedies, and keep the medicines away from direct sunlight, heat strong smells and perfumes and do not store them in the fridge.Curing without any side effects or complications thats the beauty of Homeopathic Medicine Homeopathic remedies are available over the counter at most Health and Herb Stores in USA and EU.

Take Care and Go

### Which girls and boys names do you like best?

Girls: elizabeth, Clara, baye, anabelle, Rosalie (Rosie), Allison (Allie), Mackenzie, Scarlett, Annie, Caroline

Boys: Jacob, Charlie, James, Parker, Matthew, Chris, William, Bryce, Noah, Levi

Please rank 1-10 one being the best! Thanks for all your help!

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In this order starting with my favorite:

Girls: Elizabeth, Anabelle, Annie, Clara, Scarlett, Rosalie, Mackenzie, Caroline, Baye, Allison

Boys: Matthew, Noah, Chris, Charlie, James, William, Jacob, Parker, Levi, Bryce