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"If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems." -John Grego, University of South Carolina "Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods" -Prof. David Blackwell, Department of Statistics, University of California, Berkeley Praise for the first edition: "A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life." -Robert Matthews, Aston University, in New Scientist "an essential reference text for any applied statistician" -Stephen Brooks, University of Cambridge, in The Statistician "will contribute to closing the gap between scientists and statisticians" -Sander Greenland, UCLA, in American Journal of Epidemiology "an excellent teaching reference for advanced undergraduate and graduate courses" -Nicky Best, Imperial College School of Medicine, in Statistics in Medicine
Product Description
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analysis from a Bayesian perspective. Changes in this edition include: additional material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, a chapter on advanced computation topics, more examples, and additional chapters on current models for Bayesian data analysis, such as equation models and generalized linear mixed models. This is both an introductory textbook and a reference working scientists will use throughout their professional life
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"If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems." -John Grego, University of South Carolina "Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods" -Prof. David Blackwell, Department of Statistics, University of California, Berkeley Praise for the first edition: "A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life." -Robert Matthews, Aston University, in New Scientist "an essential reference text for any applied statistician" -Stephen Brooks, University of Cambridge, in The Statistician "will contribute to closing the gap between scientists and statisticians" -Sander Greenland, UCLA, in American Journal of Epidemiology "an excellent teaching reference for advanced undergraduate and graduate courses" -Nicky Best, Imperial College School of Medicine, in Statistics in Medicine
Product Description
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analysis from a Bayesian perspective. Changes in this edition include: additional material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, a chapter on advanced computation topics, more examples, and additional chapters on current models for Bayesian data analysis, such as equation models and generalized linear mixed models. This is both an introductory textbook and a reference working scientists will use throughout their professional life
See all Product Description
Inside This Book
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First Sentence
By Bayesian data analysis, we mean practical methods for making inferences from data using probability models for quantities we observe and for quantities about which we wish to learn. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index
By Bayesian data analysis, we mean practical methods for making inferences from data using probability models for quantities we observe and for quantities about which we wish to learn. Read the first page
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Browse Sample PagesConcordance
Front Cover | Copyright | Table of Contents | Excerpt | Index
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