Download or read book Bayesian Inference and Decision Techniques written by P. K. Goel and published by North Holland. This book was released on 1986 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians from several countries. Part I of this book relates most directly to de Finetti's interests whilst Part II deals specifically with the implications of the assumption of finitely additive probability. Parts III & IV discuss applications of Bayesian methodology in econometrics and economic forecasting, and Part V examines assessment of prior parameters in specific parametric setting and foundational issues in probability assessment. The following section deals with state of the art for comparing probability functions and gives an assessment of prior distributions and utility functions. In Parts VII & VIII are a collection of papers on Bayesian methodology for general linear models and time series analysis (the most often used tools in economic modelling), and papers relevant to modelling and forecasting. The remaining two Parts examine, respectively, optimality considerations and the effectiveness of the Conditionality-Likelihood Principle as a vehicle to convince the non-Bayesians about the usefulness of the Bayesian paradigm.
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.
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.
Download or read book Modeling in Medical Decision Making written by Giovanni Parmigiani and published by John Wiley & Sons. This book was released on 2002-03 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to implement and can help to address the most pressing practical and ethical concerns arising in medical decision making. * Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory. * Driven by three real applications, presented as extensively detailed case studies. * Case studies include simplified versions of the analysis, to approach complex modelling in stages. * Features coverage of meta-analysis, decision analysis, and comprehensive decision modeling. * Accessible to readers with only a basic statistical knowledge. Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence-based medicine, health economics, health service research and health policy.
Download or read book Bayesian Inference and Decision Techniques written by Bruno De Finetti and published by . This book was released on 1900 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Introduction to Bayesian Inference and Decision written by Robert L. Winkler and published by Probabilistic Pub. This book was released on 2003-01-01 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: CD-ROM contains: Beta Distribution Generator (Excel file) ; Binomial Distribution Generator (Excel file) ; book exercises (MS Word files) ; book figures (Powerpoint files) ; TreeAge Data decision trees for some of the examples in the book ; Demonstration versions of TreeAge Data and Lumina Analytica.
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.
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.
Download or read book Risk Assessment and Decision Analysis with Bayesian Networks written by Norman Fenton and published by CRC Press. This book was released on 2012-11-07 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.
Download or read book Decision Analysis written by J. Q. Smith and published by . This book was released on 1988 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen and published by Springer Science & Business Media. This book was released on 2009-03-17 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.
Download or read book Bayesian Statistics written by S. James Press and published by . This book was released on 1989-05-10 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems. First part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. Second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. Includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay. Contains exercises.
Download or read book Comparative Statistical Inference written by Vic Barnett and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision-making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what assumptions they are based on, and the practical implications of such distinctions. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts. Includes fully updated and revised material from the successful second edition Recent changes in emphasis, principle and methodology are carefully explained and evaluated Discusses all recent major developments Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood etc) Includes extensive references and bibliography Written by a well-known and respected author, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.
Download or read book Optimal Statistical Decision Bayesian Inference in Statistical Analysis Applied Statistical Decision Theory written by Morris H. DeGroot and published by Wiley. This book was released on 2006-05-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Set that includes three works covering statistical decision theory and analysis The three books within this set are Optimal Statistical Decisions, Bayesian Inference in Statistical Analysis, and Applied Statistical Decision Theory. Optimal Statistical Decisions discusses the theory and methodology of decision-making in the field. The volume stands as a clear introduction to Bayesian statistical decision theory. A second book, Bayesian Inference in Statistical Analysis, examines the application and relevance of Bayes' theorem to problems that occur during scientific investigations, where inferences must be made regarding parameter values about which little is known. Key aspects of the Bayesian approach are discussed, including the choice of prior distribution, the problem of nuisance parameters, and the role of sufficient statistics. Applied Statistical Decision Theory covers the development of analytic techniques in the field of statistical decision theory. This classic book was first published in the 1960s.
Download or read book A First Course in Bayesian Statistical Methods written by Peter D. Hoff and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Download or read book Strategic Economic Decision Making written by Jeff Grover and published by Springer Science & Business Media. This book was released on 2012-12-05 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes’ theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes’ model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study. Very little has been published in the area of discrete Bayes’ theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.
Download or read book Bayesian Methods for Statistical Analysis written by Borek Puza and published by ANU Press. This book was released on 2015-10-01 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Methods for Statistical Analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.
