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Author : Carol Marie McDonald
Publisher :
ISBN 13 :
Total Pages : 70 pages
Book Rating : 4.:/5 (3 download)
Download or read book Bayes Solutions to Statistical Decision Problems written by Carol Marie McDonald and published by . This book was released on 1961 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Thomas Edmond Oberbeck
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (64 download)
Download or read book Bayes Solutions of Some Simple Statistical Decision Problems written by Thomas Edmond Oberbeck and published by . This book was released on with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Robert Stephen Burton
Publisher :
ISBN 13 :
Total Pages : 216 pages
Book Rating : 4.:/5 (488 download)
Download or read book Game-theoretic and Bayes Solutions of a Three-hypothesis Statistical Decision Problem written by Robert Stephen Burton and published by . This book was released on 1961 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : James O. Berger
Publisher : Springer Science & Business Media
ISBN 13 : 147574286X
Total Pages : 633 pages
Book Rating : 4.4/5 (757 download)
Download or read book Statistical Decision Theory and Bayesian Analysis written by James O. Berger and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.
Author : Ming-Hui Chen
Publisher : Springer Science & Business Media
ISBN 13 : 1441969446
Total Pages : 631 pages
Book Rating : 4.4/5 (419 download)
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Author : Abraham Wald
Publisher : Chelsea Publishing Company, Incorporated
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4.3/5 (91 download)
Download or read book Statistical Decision Functions written by Abraham Wald and published by Chelsea Publishing Company, Incorporated. This book was released on 1971 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : J. N. van Niekerk
Publisher :
ISBN 13 :
Total Pages : 125 pages
Book Rating : 4.:/5 (897 download)
Download or read book An Introduction to the Empirical Bayes Approach to Statistical Decision Problems written by J. N. van Niekerk and published by . This book was released on 1978 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Richard Park Bland
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.3/5 (91 download)
Download or read book A Minimum Average Risk Solution for the Problem of Choosing the Largest Mean written by Richard Park Bland and published by . This book was released on 1961 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of choosing the largest of n means is considered as a multiple decision problem which is generated from n component two-decision problems. With additive losses Bayes rules for the component problems yield Bayes rules for the multiple decision problem. Some properties of these Bayes rules are found. Also a co servativenear-Bayes rule is presented with tabled values for any number of means. (Author).
Author : Yoshikazu Sawaragi
Publisher : Elsevier
ISBN 13 : 148326677X
Total Pages : 231 pages
Book Rating : 4.4/5 (832 download)
Download or read book Statistical Decision Theory in Adaptive Control Systems written by Yoshikazu Sawaragi and published by Elsevier. This book was released on 2016-06-03 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics in Science and Engineering, Volume 39: Statistical Decision Theory in Adaptive Control Systems focuses on the combination of control theory with statistical decision theory. This volume is divided into nine chapters. Chapter 1 reviews the history of control theory and introduces statistical decision theory. The mathematical description of random processes is covered in Chapter 2. In Chapter 3, the basic concept of statistical decision theory is treated, while in Chapter 4, the method of solving statistical decision problems is described. The application of statistical decision concepts to control problems is explained in Chapter 5. Chapter 6 elaborates a method of designing an adaptive control system. An application of the sequential decision procedure to the design of decision adaptive control systems is illustrated in Chapter 7. Chapter 8 is devoted to the description of a method of the adaptive adjustment of parameters contained in nonlinear control systems, followed by a discussion of the future problems in applications of statistical decision theory to control processes in the last chapter. This book is recommended for students and researchers concerned with statistical decision theory in adaptive control systems.
Author : Edward J. Dudewicz
Publisher :
ISBN 13 :
Total Pages : 336 pages
Book Rating : 4.3/5 (91 download)
Download or read book Solutions in Statistics and Probability written by Edward J. Dudewicz and published by . This book was released on 1980 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : James Berger
Publisher : Springer Science & Business Media
ISBN 13 : 147571727X
Total Pages : 440 pages
Book Rating : 4.4/5 (757 download)
Download or read book Statistical Decision Theory written by James Berger and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision theory is generally taught in one of two very different ways. When of opti taught by theoretical statisticians, it tends to be presented as a set of mathematical techniques mality principles, together with a collection of various statistical procedures. When useful in establishing the optimality taught by applied decision theorists, it is usually a course in Bayesian analysis, showing how this one decision principle can be applied in various practical situations. The original goal I had in writing this book was to find some middle ground. I wanted a book which discussed the more theoretical ideas and techniques of decision theory, but in a manner that was constantly oriented towards solving statistical problems. In particular, it seemed crucial to include a discussion of when and why the various decision prin ciples should be used, and indeed why decision theory is needed at all. This original goal seemed indicated by my philosophical position at the time, which can best be described as basically neutral. I felt that no one approach to decision theory (or statistics) was clearly superior to the others, and so planned a rather low key and impartial presentation of the competing ideas. In the course of writing the book, however, I turned into a rabid Bayesian. There was no single cause for this conversion; just a gradual realization that things seemed to ultimately make sense only when looked at from the Bayesian viewpoint.
Author : F. Liese
Publisher : Springer Science & Business Media
ISBN 13 : 0387731946
Total Pages : 696 pages
Book Rating : 4.3/5 (877 download)
Download or read book Statistical Decision Theory written by F. Liese and published by Springer Science & Business Media. This book was released on 2008-12-30 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: For advanced graduate students, this book is a one-stop shop that presents the main ideas of decision theory in an organized, balanced, and mathematically rigorous manner, while observing statistical relevance. All of the major topics are introduced at an elementary level, then developed incrementally to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. With its broad coverage of decision theory, this book fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory.
Author : Robert E. Dear
Publisher :
ISBN 13 :
Total Pages : 64 pages
Book Rating : 4.:/5 (227 download)
Download or read book Bayes Decision Procedures for Stimulus Sampling Models: I. Nonsequential Experimentation written by Robert E. Dear and published by . This book was released on 1964 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: In stimulus sampling models of learning, the probability distribution defined on the sequence of conditioning functions which are used in these models may be regarded as a distribution over parameters. Consequently, this probability distribution is interpreted as an a priori distribution and the appropriateness of Bayes decision procedures for solving statistical decision problems involving these models is shown. Using beta distributions as a tractable family of prior distributions over the parameters of the single element model, Bayes solutions are illustrated to: (1) the learning criterion problem, (2) parameter estimation problems, and (3) the optimal design of a learning experiment. (Author).
Author : James John Martin
Publisher :
ISBN 13 :
Total Pages : 224 pages
Book Rating : 4.F/5 ( download)
Download or read book Bayesian Decision Problems and Markov Chains written by James John Martin and published by . This book was released on 1967 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book ... deals with a theoretical foundation for the solution of decision problems in a Markov chain with uncertain transition probabilities and considers both sequential sampling and fixed-sample-size problems." -- Preface.
Author : Jim Q. Smith
Publisher : Cambridge University Press
ISBN 13 : 1139491113
Total Pages : 349 pages
Book Rating : 4.1/5 (394 download)
Download or read book Bayesian Decision Analysis written by Jim Q. Smith and published by Cambridge University Press. This book was released on 2010-09-23 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.
Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author : Shelemyahu Zacks
Publisher : John Wiley & Sons
ISBN 13 : 1118605837
Total Pages : 499 pages
Book Rating : 4.1/5 (186 download)
Download or read book Examples and Problems in Mathematical Statistics written by Shelemyahu Zacks and published by John Wiley & Sons. This book was released on 2013-12-17 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides the necessary skills to solve problems in mathematical statistics through theory, concrete examples, and exercises With a clear and detailed approach to the fundamentals of statistical theory, Examples and Problems in Mathematical Statistics uniquely bridges the gap between theory andapplication and presents numerous problem-solving examples that illustrate the relatednotations and proven results. Written by an established authority in probability and mathematical statistics, each chapter begins with a theoretical presentation to introduce both the topic and the important results in an effort to aid in overall comprehension. Examples are then provided, followed by problems, and finally, solutions to some of the earlier problems. In addition, Examples and Problems in Mathematical Statistics features: Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving More than 430 unique exercises with select solutions Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers.
