Currently, there are about 40 to 60 million Americans suffering from Dry Eye Syndrome (DES); this serious public health problem will worsen with the explosive aging population created by baby boomers, where DES has a high incidence. However, the therapeutics for DES are elusive because our understanding of DES is so elementary, especially the correlation between symptoms and the diagnosis. Unfortunately, a quantitative diagnosis, which is the prerequisite to advance the management of DES, is yet to be realized. We are seeking the next breakthrough in DES management by providing a quantitative diagnosis, with the combination of optical coherence tomography (OCT) imaging and statistical decision theory. The statistical decision theory is formulated for the specific task of the tear film imaging, thus it will allow the assessment and optimization of the OCT system for such an application. Furthermore, the combination of the statistical decision theory and OCT will show its advantage by taking into ...
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Decision theory an introduction to dynamic programming and sequential decisions pdf, Decision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK. Mathematical induction, and its use. DECISION THEORY: AN INTRODUCTION TO DYNAMIC. PROGRAMMING AND SEQUENTIAL DECISIONS PDF,. EPUB, EBOOK. John Bather | pages |
An extended analysis is given of the program, originally suggested by Deutsch, of solving the probability problem in the Everett interpretation by means of decision theory. Deutschs own proof is discussed, and alternatives are presented which are based upon different decision theories and upon Gleasons Theorem. It is argued that decision theory gives Everettians most or all of what they need from `probability. Contact is made with Lewiss Principal Principle linking subjective credence with objective chance: an Everettian Principal Principle is formulated, and shown to be at least as defensible as the usual Principle. Some consequences of (Everettian) quantum mechanics for decision theory itself are also discussed.. ...
A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. The probability distribution of a random variable, such as X, which is We can calculate the expected squared prediction error by integrating the loss function over x and y: Where P(X, Y) is the joint probability distribution in input and output. With nearest neighbors, for each x, we can ask for the average of the ys where the input, x, equals a specific value. 2. Bayesian Decision Theory •Fundamental statistical approach to statistical pattern classification •Quantifies trade-offs between classification using probabilities and costs of decisions •Assumes all relevant probabilities are known. The Theory of Statistical Decision. /Filter /FlateDecode We can express the Bayesian Inference as: posterior∝prior⋅li… 55-67. In unsupervised learning, classifiers form the backbone of cluster analysis and in supervised or semi-supervised learning, classifiers ...
The concept of rationality is a common thread through the human and social sciences - from political science to philosophy, from economics to sociology, from management science to decision analysis. But what counts as rational action and rational behavior? This book explores decision theory as a theory of rationality. Decision theory is the mathematical theory of choice and for many social scientists it makes the concept of rationality mathematically tractable and scientifically legitimate. Yet rationality is a concept with several dimensions and the theory of rationality has different roles to play. It plays an action-guiding role (prescribing what counts as a rational solution of a given decision problem). It plays a normative role (giving us the tools to pass judgment not just on how a decision problem was solved, but also on how it was set up in the first place). And it plays a predictive/explanatory role (telling us how rational agents will behave, or why they did what they did). This book shows,
Info-Gap Decision Theory: Decisions Under Severe Uncertainty by Ben-Haim, Yakov available in Hardcover on Powells.com, also read synopsis and reviews. Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses...
Heres a decision theoretic picture of how to make the decision between A and B. First, gain as much knowledge K as is reasonably possible about the laws and present conditions in the universe. The more information, the better our decision is likely to be (cf. Goods Theorem). Then calculate the conditional expected utility of the future given A with K, and do the same for B. Then do the action where the conditional expected utility is higher.. Let U(A,K) and U(B,K) be the two conditional expected utilities. (Note: I mean this to be neutral between causal and epistemic decision theories, but if I have to commit to one, itll be causal.) We want to make our decision on U(A,K) and U(B,K) for the most inclusive K we can.. Now imagine that we could ask an angel for any piece of information I about the present and the laws (e.g., by asking How many hairs do I have on my head?), and then form a new set of information K2 including I on which to calculate U(A,K2) and U(B,K2). Then we should ask for as ...
Decision theory has many practical applications. You can use it within HR and management to decide what to do in any given situation.
a) Danielle Bassett Complex Systems, Network Science, Computational Neuroscience, Systems Biology, Dynamical Systems, Soft Materials, Behavioral Network Science.. b) Larry Brown Statistical decision theory, Statistical inference, Nonparametric function estimation, Foundations of statistics, Sampling theory (census data), Empirical queueing science.. c) Andreas Buja Multidimensional Scaling, Multivariate Analysis, Statistical Inference after Model Selection, High-Dimensional Data Visualization.. d) Tony Cai High dimensional statistical inference, Nonparametric function estimation, Large-scale multiple testing, Wavelet methodology and applications, Functional data analysis, Statistical decision theory.. e) Dean Foster Variable selection,Learning models,Evolution and games.. f) Ed George Hierarchical modeling, model uncertainty, shrinkage estimation, treed modeling, variable selection, wavelet regression.. g) Phil Gressman Harmonic analysis and geometry.. h) Michael Kearns Machine learning, ...
This compendium aims at providing a comprehensive overview of the main topics that appear in any well-structured course sequence in statistics for business and economics at the undergraduate and MBA levels. The idea is to supplement either formal or informal statistic textbooks such as, e.g., Basic Statistical Ideas for Managers by D.K. Hildebrand and R.L. Ott and The Practice of Business Statistics: Using Data for Decisions by D.S. Moore, G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with a couple of extra examples. ...
Your download advances in were a tool that this Reading could even allow. How can answer, a integration viewed into our Various kindness and developmental to recommend was up, find our injuries and the homepage at cytotoxic? Marc Barasch is up to Install help to online real undead activities, and is a important, similar warning of the hoodoo for world in a conceptual that perhaps has it.
The design and operation of water resource systems are affected to a great extent by the uncertainty of future events, as stressed throughout this text. In spite of the immense variability in the...
This paper introduces a logical analysis of convex combinations within the framework of Łukasiewicz real-valued logic. This provides a natural link between the fields of many-valued logics and...
With the rapid increase in number and size of junior colleges, administrators must take advantage of the decision-making tools already used in business and industry. This study investigated how these quantitative techniques could be applied to junior college problems. A survey of 195 California junior college administrators found that the problems for which technical help would be most welcome were (1) projecting enrollment, plant, and staff needs, (2) scheduling use of staff and facilities, (3) registration and enrollment, (4) organization, communications, and personnel relations, (5) selection of staff, (6) particular phases of curriculum, counseling, instruction, finance, admissions, and student activities. Specific techniques considered here were linear and dynamic programming, queueing and game theory, Monte Carlo and computer simulation, symbolic logic, matrix algebra, statistical decision theory, quality control charts and sampling plans, factor analysis, and PERT (Program
Signal detection theory, as developed in electrical engineering and based on statistical decision theory, was first applied to human sensory discrimination about 40 years ago. The theorys intent was to explain how humans discriminate and how we might use reliable measures to quantify this ability.
Books by Lucien M. Le Cam, Théorie asymptotique de la décision statistique, Asymptotic methods in statistical decision theory, Asymptotics in statistics, Locally asymptotically normal families of distributions, Convergence in distribution of stochastic processes, On some asymptotic properties of maximum likelihood estimates and related Bayes estimates
TY - JOUR. T1 - Investigation of statistical decision rules for sequential hematologic laboratory tests. AU - Klee, G. G.. AU - Ackerman, E.. AU - Elveback, L. R.. AU - Gatewood, L. C.. AU - Pierre, R. V.. AU - OSullivan, M. B.. N1 - Copyright: Copyright 2017 Elsevier B.V., All rights reserved.. PY - 1978. Y1 - 1978. N2 - A statistical data processing program for identifying patients who are likely to have abnormalities on various hematologic laboratory tests is described. The prediction of abnormal levels of serum vitamin B12, serum folate, transferrin saturation, and reticulocyte counts is based on a statistical analysis of the patients age, sex, and routine blood cell measurements. The program was developed using data from normal-value studies and data from patients who had these laboratory abnormalities. The sensitivity and specificity of the program were evaluated in a controlled prospective study of about 5,000 ambulatory adult patients. The programs predictions also were compared with ...
When I first thought of putting Science and Technology Studies (STS) and archaeology in conversation, several aspects of this conversation seemed obvious. Given that things and human interactions with things are central to both fields of inquiry, I thought that it would largely be a discussion about epistemologies and the way that the social, as a field of action, is constructed by both disciplines. Indeed, the reason I thought about this conversation was that both STS and archaeology seem to infer aspects of the social from human-thing relationships. Further, archaeology has increasingly, in its turn to materiality and embrace of new materialism, conceptualized things as assemblages, nodes within networks of practice, flows of materials and the socially constructed physical properties of substances, and the ability of things to act, as conceived of in theories of symmetric agency and actor-network theory (ANT), (e.g., Knappet 2011, Malafouris 2008, Olsen and Witmore 2015, Renfrew ...
The Diffusion of Accounting Innovations in the New Public Sector as Influenced by IMF Reforms: Actor-Network Theory: 10.4018/IJANTTI.2016100103: This paper aims to explain the diffusion of management accounting innovations within the public sector in Jordan as influenced by IMF reforms. It is concerned
Bayesian Statistics (10394-711). Objectives and content: The aim of the module is to introduce the students to the basic principles of Bayesian Statistics and its applications. Students will be able to identify the application areas of Bayesian Statistics. The numerical methods often used in Bayesian Analysis will also be demonstrated. Topics: Decision theory in general; risk and Bayesian risk in Bayesian decisions; use of non-negative loss functions; construction of Bayesian decision function; determining posteriors; sufficient statistics; class of natural conjugate priors; marginal posteriors; class of non-informative priors; estimation under squared and absolute error loss; Bayesian inference of parameters; Bayesian hypothesis testing; various simulation algorithms for posteriors on open source software; numerical techniques like Gibbs sampling and the Metropolis-Hastings algorithm, as well as MCMC methods to simulate posteriors.. Biostatistics (10408-712). Objectives and content: ...
Downloadable! In decision theory, incommensurabilities among conflicting decision criteria are typically handled by multicriteria optimization methods such as Pareto efficiency and mean-variance analysis. In econometrics and statistics, where conflicting model criteria replace conflicting decision criteria, probability assessments are routinely used to transform disparate model discrepancy terms into apparently commensurable quantities. This tactic has both strengths and weaknesses. On the plus side, it permits the construction of a single real-valued measure of theory and data incompatibility in the form of a likelihood function or a posterior probability distribution. On the minus side, the amalgamation of conceptually distinct model discrepancy terms into a single real-valued incompatibility measure can make it difficult to untangle the true source of any diagnosed model specification problem. This paper discusses recent theoretical and empirical work on a multicriteria ``flexible least squares
Authors: George Loewenstein, Carnegie Mellon University; Jennifer S. Lerner, Carnegie Mellon University. Publication: Handbook of Affective Science. Year: 2003. Focus Area: Decision Making, Emotion. Relevance: This textbook chapter on emotions involvement in decision making provides a comprehensive introduction to the study of decision making. A number of common terms in decision research are defined in this chapter, often with easily understood examples.. Summary: Traditional decision theory focused on cognitive decision making but largely ignored the importance of emotions in decision making until the late 1980s, but now contemporary decision research is characterized by an intense focus on emotion. Current theory identifies two kinds of emotions that influence decisions: expected emotions, which are expectations about future emotional consequences of a decision, and immediate emotions, which are emotions felt while making a decision.. ...
An overview of the principles and construction regarding the decision tree is provided as well as a the decision theory regarding the decision tree analysis. Often, we do care about risk and want to avoid painful outcomes. These options varied randomly from trial to trial.. Alternative 1: -100 Alternative 2: -200 Alternative 3: -250 Using Maximin, we should select alternative 1 since it has the best worst case outcome. The exponential utility function is a common function used to convert payoff values into utility.. Simon Deakin and the Centre for Business Research for ethical oversight, and Zeina Afif for her feedback on the manuscript.. So, find the maximum of the regrets for each of the strategies and then find the strategy which has the minimum of all the maximum regrets so then the second is to find the minimum amongst those regrets. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.. Lebanon is rapidly adapting ...
Probabilistic Modelling as Decision Making. One of the factors that limits application of probabilistic reliability to engineering design is the arbitrariness with which probability distribution may be chosen. The problem is particularly severe when high reliability is demanded and information is scarce, e.g., in selecting tail probability laws for load and resistance variables. At the origin of this arbitrariness is the attempt to select models through inference procedures, i.e., solely on the basis of physical and statistical information. The problem is, by its very nature, one of decision making and should be resolved through methods of decision theory. If this is done, then a number of interesting facts emerge: (1)The optimal distribution is also defined under conditions of extreme uncertainty; (2)the optimal distribution is less sensitive to statistical sample variations than the distribution obtained by inference procedures; and (3)with limited statistical information, optimal distributions
Probability and statistics were the only well-founded theories of uncertainty for a long time. However, during the last fifty years, in such areas like decision theory, artificial intelligence or information processing, numerous approaches extending or orthogonal to the existing theory of probability and mathematical statistics have been successfully developed. These new approaches have appeared, either on their own like fuzzy set theory, possibility theory, rough sets, or having their origin in probability theory itself, like imprecise probability, belief functions, fuzzy random variables.. The common feature of all those attempts is to allow for a more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The proposed new methods are softer than the traditional theories and techniques because being less rigid they more easily adapt to the actual nature of information.. Wide range of applications still reveals the need for soft extensions of classical probabilistic and ...
13.1 Individual decision making in the face of risk 13.2 Option price and option value 13.3 Risk and irreversibility 13.4 Environmental cost-benefit analysis revisited 13.5 Decision theory: choices under uncertainty 13.6 A safe minimum standard of conservation. Slideshow 1356617 by kaori
TY - JOUR. T1 - Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference. AU - Ting, Chih Chung. AU - Wu, Shih Wei. AU - Wu, Shih Wei. AU - Yu, Chia Chen. AU - Maloney, Laurence T.. AU - Maloney, Laurence T.. AU - Maloney, Laurence T.. N1 - Publisher Copyright: © 2015 the authors.. PY - 2015/1/28. Y1 - 2015/1/28. N2 - In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. ...
Preface. 1. Introduction.. 1.1 Two Examples.. 1.1.1 Public School Class Sizes.. 1.1.2 Value at Risk.. 1.2 Observables, Unobservables, and Objects of Interest.. 1.3 Conditioning and Updating.. 1.4 Simulators.. 1.5 Modeling.. 1.6 Decisionmaking.. 2. Elements of Bayesian Inference.. 2.1 Basics.. 2.2 Sufficiency, Ancillarity, and Nuisance Parameters.. 2.2.1 Sufficiency.. 2.2.2 Ancillarity.. 2.2.3 Nuisance Parameters.. 2.3 Conjugate Prior Distributions.. 2.4 Bayesian Decision Theory and Point Estimation.. 2.5 Credible Sets.. 2.6 Model Comparison.. 2.6.1 Marginal Likelihoods.. 2.6.2 Predictive Densities.. 3. Topics in Bayesian Inference.. 3.1 Hierarchical Priors and Latent Variables.. 3.2 Improper Prior Distributions.. 3.3 Prior Robustness and the Density Ratio Class.. 3.4 Asymptotic Analysis.. 3.5 The Likelihood Principle.. 4. Posterior Simulation.. 4.1 Direct Sampling,.. 4.2 Acceptance and Importance Sampling.. 4.2.1 Acceptance Sampling.. 4.2.2 Importance Sampling.. 4.3 Markov Chain Monte ...
Newcombs Problem. A central problem in decision theory. Imagine an agent that understands psychology well enough to predict your decisions in advance, and decides to either fill two boxes with money, or fill one box, based on their prediction. They put \$1,000 in a transparent box no matter what, and they then put \$1 million in an opaque box if (and only if) they predicted that youd only take the opaque box. The predictor tells you about this, and then leaves. Which do you pick? If you take both boxes, you get only the \$1000, because the predictor foresaw your choice and didnt fill the opaque box. On the other hand, if you only take the opaque box, you come away with \$1M. So it seems like you should take only the opaque box. However, many people object to this strategy on the grounds that you cant causally control what the predictor did in the past; the predictor has already made their decision at the time when you make yours, and regardless of whether or not they placed the \$1M in the opaque ...
The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching ...
The qualifications and skills obtained during master programs often hardly prepare students to conduct eye-tracking studies, to avoid potential pitfalls when using the eye-tracking equipment and to analyze the complex eye-tracking datasets. Especially in the beginning of a PhD project these challenges appear to be overwhelming. PhD students completing the course will gain an overview of research in the field of bottom-up and top-down attentional process and search in decision-making. We will give an overview on latest developments in the field, including learning and contextual biases in decision sequences and the evaluation of decision theories. From a practical perspective PhD students will get insight in the process of setting up eye-tracking experiments, conducting a first empirical study on their own and analyzing an eye-tracking dataset. PhD students will have the opportunity to use remote eye-tracking devices together with their own laptops and use the provided software to analyze their ...
In this conference, investigators present topics that might be empirical or theoretical, involving questions that may be basic or applied, and studying theories that may be normative or descriptive. Topics deal with judgment and decision theory, basic and applied, either normative or descriptive, and are NOT limited to Bayes theorem or Bayesian statistics.
Olivier is an economist and global expert in decision theory under uncertainty, with applications in development economics, climate finance and disaster risk finance and insurance. Olivier leads the development of innovative (parametric) sovereign risk finance and insurance solutions. He is one of the main architects of sovereign catastrophe risk pools such as the Caribbean Catastrophe Risk Insurance Facility (CCRIF), the Pacific Catastrophe Risk Insurance Company (PCRIC) and the Southeast Asia Disaster Risk Insurance Facility (SEADRIF). He also co-designed the first sovereign contingent credit line against disasters. Olivier currently leads the Crisis & Disaster Risk Finance Unit and its Disaster Risk Financing and Insurance Program (DRFIP) at the World Bank, which offers analytical and advisory services for policy reforms and financial instruments against climate shocks, disasters and a wider range of crises in more than 60 emerging and developing countries all over the globe.. Olivier also ...
Terminology related to risk and uncertainty in projects. Main elements of risk management to deal with risk and uncertainty in projects. Review of central practical methods for identification and analysis of risk and uncertainty. Quantitative modelling of schedule and time in project. Decision theory. Cost analysis in a life time perspective. Statistical methods for parameter estimation. Use of expert judgement for assessment of parameters. Case studies related to turnarounds.. ...
g. The distance between Gainesville, Florida, and all Florida cities with a population of atleast 50,000.. What are the requirements (or characteristics) for the binomial and Poisson? Under what conditions will the binomial and the Poisson distributions give roughly the same results? Show with an example.. What is decision theory? What is the difference between a payoff table and an expected payoff table? In the following payoff table, let P(S1) = 0.30, P(S2) = 0.50, and P(S3) = 0.20. Compute the expected monetary value for each of the alternatives. What decision would you recommend?. A foreman thinks that the low efficiency of the machine tool operators is directly linked to the high level of fumes emitted in the workshop. He would like to prove this to his supervisor through a research study.. 1. Would this be a causal or a correlational study? Why?. 2. Is this an exploratory, descriptive, or hypothesis-testing (analytical or predictive) study? Why?. 3. What kind of a study would this be: ...
How do we make decisions? Conventional decision theory tells us only which behavioral choices we ought to make if we follow certain axioms. In real life, however, our choices are governed by cognitive mechanisms shaped over evolutionary time through the process of natural selection. Evolution has created strong biases in how and when we process information, and it is these evolved cognitive building blocks-from signal detection and memory to individual and social learning-that provide the foundation for our choices ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word reinforcement. The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some
The concept of acausal trade emerged out of the much-debated question of how to achieve cooperation on a Prisoners Dilemma, where, by design, the two players are not allowed to communicate. On the one hand, a player in the one-shot Prisoners Dilemma who is considering the causal consequences of a decision finds that defection always produces a better result. On the other hand, if the other player symmetrically reasons this way, the result is an equilibrium of Defect/Defect, which is bad for both agents. If they can somehow converge on mutual cooperation, they will each do better, on their own individual utility measure, than the Defect/Defect equilibrium. The question is what decision theory allows this beneficial cooperation equilibrium. Douglas Hofstadter (see references) coined the term super-rationality to express this state of convergence. He illustrated it with a game in which twenty players, who do not know each others identities, each get an offer. If exactly one player asks for the ...
Aaronson, Scott (2004) Is Quantum Mechanics An Island in Theoryspace? [Preprint] Aaronson, Scott (2011) Why Philosophers Should Care About Computational Complexity. [Preprint] Afriat, Alexander (2002) Altering the remote past. [Preprint] Afriat, Alexander (2004) Duhem, Quine and the other dogma. [Preprint] Afriat, Alexander (2008) Duhem, Quine and the other dogma. [Preprint] Afriat, Alexander (2004) If Bertlmann had three feet. [Preprint] Afriat, Alexander (2013) Is the world made of loops? [Preprint] Afriat, Alexander (2012) Logic of gauge. [Preprint] Ahmed, Arif and Caulton, Adam (2014) Causal Decision Theory and EPR correlations. [Preprint] Allori, Valia (2015) How to Make Sense of Quantum Mechanics(and More):Fundamental Physical Theories and Primitive Ontology. [Preprint] Allori, Valia (2012) On the Metaphysics of Quantum Mechanics. [Preprint] Allori, Valia (2012) Primitive Ontology and the Structure of Fundamental Physical Theories. [Preprint] Allori, Valia and Goldstein, Sheldon and ...
Patrick Colonel Suppes (/ˈsʊpɪs/; March 17, 1922 - November 17, 2014) was an American philosopher who made significant contributions to philosophy of science, the theory of measurement, the foundations of quantum mechanics, decision theory, psychology and educational technology. He was the Lucie Stern Professor of Philosophy Emeritus at Stanford University and until January 2010 was the Director of the Education Program for Gifted Youth also at Stanford. Suppes was born on March 17, 1922, in Tulsa, Oklahoma. He grew up as an only child, later with a half brother George who was born in 1943 after Patrick had entered the army. His grandfather, C.E. Suppes, had moved to Oklahoma from Ohio. Suppes father and grandfather were independent oil men. His mother died when he was a young boy. He was raised by his stepmother, who married his father before he was six years old. His parents did not have much formal education. Suppes began college at the University of Oklahoma in 1939, but transferred to ...
Lecture, three hours; discussion, one hour; laboratory, one hour. Requisite: course 115A, 164, 170A and Programming in Computing 10A. An introductory course on mathematical models for pattern recognition and machine learning. Topics include parametric and non-parametric probability distributions, the curse of dimensionality, correlation analysis and dimensionality reduction, and concepts of decision theory. Advanced machine learning and pattern recognition problems including data classification and clustering, regression, kernel methods, artificial neural networks, hidden Markov models and Markov random fields. Projects will be in Matlab that will a part of the final project presented in class. P/NP or letter grading.. ...
Developing and applying statistical inference, decision theory & optimisation, machine learning and visualisation techniques that will process data and draw new insights and knowledge from the data.. Bayesian Inference. ...
Previously, several types of PTMs have been investigated using computational approaches, e.g. phosphorylation, glycosylation, sulfation and myristoylation, etc. However, the prediction performances of these programs still remain to be improved. The Cuckoo Workgroup focused on developing more rigorous computational models and designing more efficient algorithms to enhance the research of PTMs. Besides the well-know PTM of phosphorylation, we also considered several other new PTMs, including sumoylation, palmitoylation and Lysine/Arginine methylation, etc. We developed several easy-to-use online web tools and downloadable softwares. For example, we constructed GPS and PPSP for prediction of phosphorylation sites, based on GPS and Bayesian Decision Theory algorithms, respectively. And we designed the CSS-Palm to predict the palmitoylation sites. Also, we developed the online tool of MeMo to predict Lysine/Arginine methylation sites, with SVMs algorithm. Moreover, we constructed an online tool of ...
Aitchison, J. and Dunsmore, I. R. (1975) Statistical Prediction Analysis, Cambridge, Cambridge University Press.. Berger, J. O. (1985) Statistical Decision Theory and Bayesian Analysis Springer, New York.. Berger, J. O. & Berliner, M. (1986): Robust Bayes and empirical Bayes analysis with Î-contaminated priors, The Annals of Statistics, 14 (2), pp. 461-486.. Berry, B., Elliot, E., and Harpham, E. J. (1996) The yield curve as an electoral bellwether, Technical forecasting and social change, 51, pp. 281-294.. Erikson, R. S., and Wlezien, C. (1996) Of time and presidential election forecasts PS: Political Science and politics, 31, pp. 37-39.. Fair, R. C. (1978) The effect of economic events on votes for president, Review of Economics and Statistics, 60, pp. 159-173. Fair, R. C. (1996) The effect of economic events on votes for president: 1992 update, Political Behavior, 18, pp. 119-139 Fair, R. C. (2002) Predicting Presidential Elections and Other Things, Stanford: Stanford University ...
Here we examined whether these biomarkers may aid in the classification of It works with continuous and/or categorical predictor variables. It is also seen that a one-unit increase in the predictive variable of epistemological belief leads to an increase of 4.9% in high critical thinking odds. as μ (MOR) and κ (KOR) opioid receptors. This could facilitate females memorization of these highly individualized acoustic parameters to identify their offsprings call in both media. An exhaustive analysis was performed on the different call types, and individual vocal signatures were investigated. The homogeneity of a variance-covariance matrix is assessed using Box-M statistics, ... Each stepwise discriminant analysis was performed to select the most correlated predictors from preserved dimensions in the corresponding KSD-VP-1/1 vertebra. Probability Density Estimation Bayes Decision Theory - Binary Features. Plotting the resulting canonical variables showed the cistern … including the concept of ...
The book starts with basic topics, such as formulation and graphical solution of Linear Programming Problems (LPP), simplex and revised Simplex Method, duality and sensitivity analysis, transportation and assignment models, and then moves on to advance topics, such as sequencing and scheduling (CPM &PERT), dynamic, integer and goal programming, game and decision theories, queuing and replacement models, simulation, inventory (deterministic an
Scholarly Communication is located on the fourth floor of Carol M. Newman Library at Virginia Tech. Scholarly Communication is a dynamic landscape, and we are continually evolving. Many scholarly communications activities have spun-off into their own departments, such as VT Publishing and Digital Imaging and Preservation Services, and Digital Library Development. Our focus is on supporting the creation and dissemination of scholarship.
This chapter reviews probability theory, limiting the discussion to the study of random events as opposed to random processes, the latter being a sequence of random events extended over a period of time. The goal is to raise the level of approach by demonstrating the usefulness of delta functions. The chapter presents a calculation of the chi-squared distribution (important in statistical decision making) with delta functions. The normalisation condition of the probability density in chi-square leads to a geometric result; namely, the volume of a sphere in n dimensions can be determined without ever transferring to spherical coordinates. This chapter also discusses the first and second laws of gambling, along with distribution functions, stochastic variables, expectation values for single random variables, characteristic functions and generating functions, measures of dispersion, joint events, conditional probabilities and Bayes theorem, sums of random variables, fitting of experimental observations,
Ahlswede, Rudolf. A constructive proof of the coding theorem for discrete memoryless channels with feedback. Transactions of the Sixth Prague Conference on Information Theory, Statistical Decision Functions, Random Processes: held at Prague, from September 19 to 25, 1971. Ed. Jaroslav Kozesník. München: Verl. Dokumentation [u.a.], 1973. 39-50 ...
Ahlswede, R. (1973): A constructive proof of the coding theorem for discrete memoryless channels with feedback. In: Jaroslav Kozesník (Hrsg.): Transactions of the Sixth Prague Conference on Information Theory, Statistical Decision Functions, Random Processes: held at Prague, from September 19 to 25, 1971. München: Verl. Dokumentation [u.a.]. S. 39-50 ...
These ten contributions describe the major technical ideas underlying many of the significant advances in natural-language processing over the last decade, focusing in particular on the challenges in areas such as knowledge representation, reasoning, planning, and integration of multiple knowledge sources, where NLP and AI research intersect. Included are chapters that deal with all the main aspects of natural-language processing, from analysis to interpretation to generation. Fruitful new relations between language research and AI such as the use of statistical decision techniques in speech and language processing are also discussed. A Special Issue of Artificial Intelligence
My technical and STS work both address aspects of the socio-technical problem of improving peoples quality of life using smart technology. The technical project aims to create a more accurate deep learning model to predict desired physical room settings (temperature, lighting, etc.) in smart buildings. The STS project uses actor-network theory to analyze what actor-networks are integral to the creation and maintenance of successful smart cities, specifically Amsterdam. Smart cities require the same machine learning framework as in smart buildings but other social factors need to be considered in understanding why some smart cities are successful. My technical work deals with individual buildings and the corresponding enabling technology, while my STS research analyzes the social factors that make large networks of smart buildings in an urban setting (smart cities) succeed. Smart building infrastructures are equipped with hundreds of sensors to monitor physical building aspects and provide smart ...
I research collaborative design, decision-making and problem-solving processes and technologies for groups that span organizational and knowledge-domain boundaries. This is the point just upstream of where business process redesign meets IT systems requirements analysis. I bring professional experience in human-centered interaction design, software engineering and requirements analysis, designing ICT systems, and integrating software architectures. My specific research interests lie in: - the co-design of business and IT systems that span organizational divisions; - the social psychology of collaboration, to reconcile diverse frames and enable collective sensemaking in wicked problem situations; - the situated use and design of technologies (including databases) to support collective processes across knowledge domains. I employ a number of theoretical-analytical lenses, including actor-network theory, technological frames, distributed cognition, and social network role analysis. The goal is to ...
Credits: Speakers Bureau of Canada www.speakerscanada.com Published on May 17, 2017 Dr. Lana Rose Potts MD CCFP is a family physician at the Siksika Health and Wellness Centre. Siksika is a First Nation near Calgary. Dr. Potts is a charter class graduate of The Northern Ontario School of Medicine in Thunder Bay, Ontario and she […]
I will first review what is known about the set of Gibbs states of the Ising and Potts models, mentioning joint works with H. Duminil-Copin, D. Ioffe and Y. Velenik for the 2d case. A natural question (open in general) is to determine whether any Gibbs state is weak limit of finite-volume measures with deterministic boundary conditions. I will give a counter-example in the 3d Ising case, and point out what the issues are in order to extend it to the Potts model. Ill end with a few conjectures.. ...
I will first review what is known about the set of Gibbs states of the Ising and Potts models, mentioning joint works with H. Duminil-Copin, D. Ioffe and Y. Velenik for the 2d case. A natural question (open in general) is to determine whether any Gibbs state is weak limit of finite-volume measures with deterministic boundary conditions. I will give a counter-example in the 3d Ising case, and point out what the issues are in order to extend it to the Potts model. Ill end with a few conjectures.. ...
material researcher alice potts, has developed a process that transforms sweat from natural perspiration into crystals on surfaces of materials.
MINNEAPOLIS (AP)Treyson Potts picked up for Minnesota where injured star Mo Ibrahim left off, rushing for 178 yards and two touchdowns in his first career start to help fend off Miami of Ohio 31-26 on Saturday. Tanner Morgan connected with Dylan Wright and Daniel Jackson on scoring passes in the second quarter as the Gophers […]
Blackberries on Horseback by Charles Potts Blackberries on Horseback Up and down Blue Creek I pick Blackberries Ripe in the August sun. Shiloh takes a bite Gets his lips hung up On the stickers. He swings his neck way out For Ocean Spray and Way down for Chicory.
We offer a collection of Debakey Potts Scissors instruments. Go to the website to buy advanced surgical instruments and medical, surgery tools online.
Standard statistical loss functions, such as mean-squared error, are commonly used for evaluating financial volatility forecasts. In this paper, an alternative evaluation framework, based on probability scoring rules that can be more closely tailored to a forecast users decision problem, is proposed. According to the decision at hand, the user specifies the economic events to be forecast, the scoring rule with which to evaluate these probability forecasts, and the subsets of the forecasts of particular interest. The volatility forecasts from a model are then transformed into probability forecasts of the relevant events and evaluated using the selected scoring rule and calibration tests. An empirical example using exchange rate data illustrates the framework and confirms that the choice of loss function directly affects the forecast evaluation results. Copyright © 2001 by John Wiley & Sons, Ltd.
Despite a plethora of promising treatments for systemic lupus erythematosus (SLE) reaching early phase clinical studies, none except belimumab has been able to show superiority in large trials and thus become available to patients. Attempts to explain this failure have pointed (among other pitfalls) to the use of problematic clinical endpoints. Lupus clinical outcome measures are sometimes organ focused (e.g. renal response definitions, CLASI, each individual domain of the BILAG index). Alternatively global scores have been used [SELENA-SLEDAI, SELENA-SLEDAI flare index, global BILAG, BILAG flare index, SLE responder index (SRI), BILAG-based Composite Lupus Assessment (BICLA)]. Much remains to be learned about the comparative performance of these endpoints, particularly the composite indices of SRI and BICLA. We have been interested in exploring the relative utility of these measures in relation to patients clinical phenotype and biologic signature (cytokine levels, gene expression ...
Comparing the relative utility of diagnostic tests is challenging when available datasets are small, partial or incomplete. The analytical leverage associated with a large sample size can be gained by integrating several small datasets to enable effective and accurate across-dataset comparisons. Accordingly, we propose a methodology for a holistic comparative analysis and ranking of cancer diagnostic tests through dataset integration and imputation of missing values, using urothelial carcinoma (UC) as a case study. Five datasets comprising samples from 939 subjects, including 89 with UC, where up to four diagnostic tests (cytology, NMP22®, UroVysion® Fluorescence In-Situ Hybridization (FISH) and Cxbladder Detect) were integrated into a single dataset containing all measured records and missing values. The tests were firstly ranked using three criteria: sensitivity, specificity and a standard variable (feature) ranking method popularly known as signal-to-noise ratio (SNR) index derived from the mean
One of the mishaps a beginner data scientist can make is not evaluating their model after building it i.e not knowing how effective and efficient their model is before deploying, It might be quite disastrous.. An evaluation metric measures the performance of a model after training. You build a model, get feedback from the metric, and make improvements until you get the accuracy you want.. Choosing the right metric is key to properly evaluate an ML model. Choosing a single metric might not be the best option, sometimes the best result comes from a combination of different metrics. Different ML use cases have different metrics. Were going to focus on classification metrics here.. Remember that metrics arent the same as loss function. The loss function shows a measure of model performance during model training. Metrics are used to judge and measure model performance after training.. One important tool that shows the performance of our model is the Confusion Matrix - its not a metric, but its as ...
Adversarial examples have pointed out Deep Neural Networks vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, based on optimal transport, which integrates this Lipschitz constraint as a theoretical requirement. We propose to learn 1-Lipschitz networks using a new loss that is an hinge regularized version of the Kantorovich-Rubinstein dual formulation for the Wasserstein distance estimation. This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound. We also prove that this hinge regularized version is still the dual formulation of an optimal transportation problem, and has a solution. We also establish several geometrical properties of this optimal solution, and extend the approach to multi-class problems. Experiments show that the proposed ...
Figure 2. Regression problems yield convex loss vs. weight plots.. Convex problems have only one minimum; that is, only one place where the slope is exactly 0. That minimum is where the loss function converges.. Calculating the loss function for every conceivable value of \(w_1\) over the entire data set would be an inefficient way of finding the convergence point. Lets examine a better mechanism-very popular in machine learning-called gradient descent.. The first stage in gradient descent is to pick a starting value (a starting point) for \(w_1\). The starting point doesnt matter much; therefore, many algorithms simply set \(w_1\) to 0 or pick a random value. The following figure shows that weve picked a starting point slightly greater than 0:. ...
More and more people are focus on the fact that tea can help weight loss as it is a healthy way.Then if all the teas can have this effect and which kind of tea is the most effective one to help lose weight?This article will share with you which kind...
Matthew has a broad, interdisciplinary background with formal training in mathematics, ecology, and economics, and more than two decades of experience in resources management issues in low- and middle-income countries. His interdisciplinary lab focuses on the co-production by human and natural systems of ecosystem services and natural pathways for carbon sequestration.. ...
Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet
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Hi, Im Lee Tran Lam. When not blogging with my mouth full, Im usually writing, presenting Local Fidelity on FBi radio, making zines, producing podcasts or continually breaking promises about how I really am gonna get through my book pile one day. All the good pictures on this blog are by photography ace (and patient boyfriend), Will Reichelt, (all the dodgy ones can be credited to me)!. The lovely banner is by friend and ultra-talented illustrator Grace Lee.. This site redesign was made possible by the next-level generosity and expertise of Daniel Boud, whose code-tinkering ways are only outranked by his seriously inspired way with a camera.. You can read more about my co-conspirators here.. This is a blog I do for pure fun and zero influence - theres no sponsorship, sneaky advertorial or advertising. I pay for all the food mentioned, cos it seems the ethical thing to do. ...
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The q-state Potts model is a spin model that has been of longstanding interest as a many body system in statistical mechanics. Via a cluster expansion, the Potts model partition function Z(G,q,v), defined on a graph G=(V,E ...
He was a military man; he made home nurse visits as a side job. I mentioned that I was planning to play a Symphony concert at Deer Valley tonight and asked if he could place the stint in the middle of my arm where I could hide it under a long-sleeved white shirt and it wouldnt bother me too much. He exclaimed, Youre planning to what? We laughed about the possible psychosis the steroids could bring on and the strange reactions I might experience at the concert -- like suddenly standing up on my chair and offering up my own rendition of Orange Blossom Special or something. I was a little nervous about how I would react to this stuff. ...
talk contribs uploaded File:Potts Shunt.jpg (The Potts shunt is a connection that is established between the descending portion of the aorta (on the left side of chest) to the left branch of the pulmonary artery. This Drawing is inspired by the following site (last image): http://www.med.nus.edu.s) ...
I would also like you to know that I used the first gallon of your product in my attic, to test its viability for my intended use. It worked incredibly well!! I have recommended it to friends, and hence have ordered 2 more gallons. I would recommend the product to anyone with mold issues. Excellent product ...
Josh Winckowski bounced back from his first tough outing of the year, allowing two runs in 5 2/3 innings with five strikeouts and just one walk. Tyler Olson also had a good night on the bump, tossing two scoreless frames with three strikeouts, no walks, and no hits allowed. Tate Matheny and Pedro Castellanos each had productive nights at the plate, each going 2 for 3 with a walk, with Matheny roping a triple. Joey Meneses knocked in a pair with a single and also drew a walk. Hudson Potts, ranked 22nd in the system, made his 2021 debut after spending the first part of the season on the injured list with an oblique strain. Potts got the start at third base, going 0 for 4 ...
SHEPHERDSTOWN -- Simply put, strikes from your pitcher win games. Those strikes dont have to be 90-mph fast balls, knee-buckling curveballs or even dancing
A sheer top lets us peek at Alice Pottss bra while her shorts mold to her firm bottom. This hot mom is locked and loaded for a good time as she gets naked and sinks a toy first into her hairy pussy, then into her tight anus as she uses a second toy to vibrate her clit ...
Extracting useful information from high-dimensional data is the focus of todays statistical research and practice. Penalized loss function minimization has been shown to be effective for this task both theoretically and empirically. With the vitues of both regularization and sparsity, the L1-penalized L2 minimization method Lasso has been popular. However, Lasso is often seen as not having enough regularization in the large p case. In this talk, we propose two methods that take into account side information in the penalized L2 framework, in order to bring the needed extra regularization in the large p case. First, we combine different norms including L1 to to introduce the Composite Absolute Penalties (CAP) family. CAP allows the grouping and hierarchical relationships between the predictors to be expressed. It covers and goes beyond existing works including grouped lasso and elastic nets. Path following algorithms and simulation results will be presented to compare with Lasso in terms of ...
White Rock businessman Norm Slavik, who died in a float plane crash near Potts Lagoon off Port McNeill on Thursday, was reluctant to make the trip, and only…
Neil Ackerman and Bret Berner and Jim Biegajski and Qiang Chen and Hilary Chen and Tom Conn and Hardip Dehal and Tim Dunn and Al Ewing and Steve Fermi and Russell Ford and Priya Jagasia and Yalia Jayalakshmi and Priti Joshi and Brian Kersten and Ronald Kurnik and Tim Lake and Matt Lesho and Jan-Ping Lin and David Liu and Margarita Lopatin and Lexa Mack and Heather Messenger and Sam Morley and Michelle Oliva and Norman Parris and Russell Potts and Jeff Pudlo and Michael Reidy and Pravin Soni and Janet Tamada and Michael Tierney and Christopher Uhegbu and Prema Vijayakumar and Charles Wei and Steve Williams and Don Wilson and Christine ...
Lera RF, Potts GK, Suzuki A, Johnson JM, Salmon ED, Coon JJ, Burkard ME. Decoding Polo-like kinase 1 signaling along the kinetochore-centromere axis. Nature Chemical Biology. 2016 ;12:411-U61. ...
Well, I promised you I would keep you updated on my pilot and I am proud to announce some more cast members have just signed on. Beau Bridges, will be playing my father, Joe. Annie Potts, will be playing my mother, Elizabeth. Amanda Detmer, will be playing my best friend, Sasha. And Eric Winter, will be playing my Stepbrother, Charlie. I am beyond thrilled that I will be getting to work with these incredibly talented actors. How freaking amazing is this cast? I am so excited. Can you tell I am excited? I cant wait to start filming.. Also, we still dont have a title yet. Across the internet people are calling it Nuclear Family and Ive even read the title My Brothers Hot. Neither of these are the actual title of the pilot. I will let you know when we settle on one.. We start filming in June. I think we will probably hear if we are picked up (please God) sometime in July. The Upfronts are next week but considering we havent shot yet, no announcement will be made about this show. If we do ...
Going nowhereA brief history of how we got here -- and the road(s) aheadIndependent candidate Russ Potts has rolled out his transportation program and he doesnt flinch on the revenue side. He wants
Bankstown North, NSW. Accommodation in Bankstown North, Potts Hill, Yagoona, Yagoona West, Bankstown, Greenacre and more. Compare hotel accommodation near Bankstown North, NSW
Eventbrite - Happy Endings Comedy Club - Kings Cross presents 6.30pm Sat Nights - Happy Endings - Same show as 8.30pm, just earlier! - Saturday, 31 July 2021 at Happy Endings Comedy Club, Potts Point, NSW. Find event and ticket information.
Kremen, Claire, Williams, Neal M., Aizen, Marcelo A., Gemmill-Herren, Barbara, LeBuhn, Gretchen, Minckley, Packer, Laurence, Potts, Simon G., Roulston, Tai, Steffan-Dewenter, Ingolf, Vázquez, Diego P., Winfree, Rachael, Adams, Laurie, and 6 others ...
In recent years, there has been growing interest in understanding a persons reaction to ambiguous situations, and two similar constructs related to ambiguity,