An Introduction To Bayesian Inference And Decision PDF Download
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Author | : Robert L. Winkler |
Publisher | : Probabilistic Pub |
Total Pages | : 452 |
Release | : 2003-01-01 |
Genre | : Mathematics |
ISBN | : 9780964793842 |
Download An Introduction to Bayesian Inference and Decision Book in PDF, ePub and Kindle
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.
Author | : Robert L. Winkler |
Publisher | : Holt McDougal |
Total Pages | : 584 |
Release | : 1972 |
Genre | : Mathematics |
ISBN | : |
Download An Introduction to Bayesian Inference and Decision Book in PDF, ePub and Kindle
Author | : James O. Berger |
Publisher | : Springer Science & Business Media |
Total Pages | : 633 |
Release | : 2013-03-14 |
Genre | : Mathematics |
ISBN | : 147574286X |
Download Statistical Decision Theory and Bayesian Analysis Book in PDF, ePub and Kindle
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 | : Andrew Gelman |
Publisher | : CRC Press |
Total Pages | : 677 |
Release | : 2013-11-01 |
Genre | : Mathematics |
ISBN | : 1439840954 |
Download Bayesian Data Analysis, Third Edition Book in PDF, ePub and Kindle
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 | : Jayanta K. Ghosh |
Publisher | : Springer Science & Business Media |
Total Pages | : 356 |
Release | : 2007-07-03 |
Genre | : Mathematics |
ISBN | : 0387354336 |
Download An Introduction to Bayesian Analysis Book in PDF, ePub and Kindle
This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.
Author | : Bruce W. Morgan |
Publisher | : |
Total Pages | : 140 |
Release | : 1968 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download An Introduction to Bayesian Statistical Decision Processes Book in PDF, ePub and Kindle
Author | : Nick Heard |
Publisher | : Springer Nature |
Total Pages | : 177 |
Release | : 2021-10-17 |
Genre | : Mathematics |
ISBN | : 3030828085 |
Download An Introduction to Bayesian Inference, Methods and Computation Book in PDF, ePub and Kindle
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
Author | : Thomas Dyhre Nielsen |
Publisher | : Springer Science & Business Media |
Total Pages | : 457 |
Release | : 2009-03-17 |
Genre | : Science |
ISBN | : 0387682821 |
Download Bayesian Networks and Decision Graphs Book in PDF, ePub and Kindle
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.
Author | : J. Q. Smith |
Publisher | : |
Total Pages | : 0 |
Release | : 1988 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Decision Analysis Book in PDF, ePub and Kindle
Author | : Norman Fenton |
Publisher | : CRC Press |
Total Pages | : 516 |
Release | : 2012-11-07 |
Genre | : Business & Economics |
ISBN | : 1439809119 |
Download Risk Assessment and Decision Analysis with Bayesian Networks Book in PDF, ePub and Kindle
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.