Download Ebook Bayesian TheoryBy José M. Bernardo, Adrian F. M. Smith
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Bayesian TheoryBy José M. Bernardo, Adrian F. M. Smith
Download Ebook Bayesian TheoryBy José M. Bernardo, Adrian F. M. Smith
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This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics
- Sales Rank: #1202381 in Books
- Published on: 2000-05-18
- Released on: 2000-04-24
- Original language: English
- Number of items: 1
- Dimensions: 9.13" h x 1.39" w x 6.04" l, 1.94 pounds
- Binding: Paperback
- 610 pages
Review
"an excellent primary source for those who wish to learn about the learning and decision process in a situation of uncertainty..." (Measurement Science Technology, February 2001)
"an ideal source for all students and researchers in statistics mathematics, decision analysis, economic and business studies and all branches of science and engineering who wish to further their understanding of Bayesian statistics." (Zentralblatt Fur Didaktik der Mathematik)
"...Bayesians will find it indispensable: non-Bayesians will find, and enjoy, much thought-provoking material to challenge their orthodoxy...." (The Statistician, Vol.51, No.2, 2002)
From the Publisher
Provides a thorough account of key basic concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Presents a novel discussion of model comparison and choice from a Bayesian perspective. An overview of non-Bayesian theories is provided and each chapter contains a wide-ranging critical re-examination of controversial issues.
From the Inside Flap
Bayesian Theory José M. Bernardo Universidad de Valencia, Valencia, Spain Adrian F. M. Smith Imperial College of Science, Technology and Medicine, London, UK Bayesian Theory is the first volume of a related series of three and will be followed by Bayesian Computation, and Bayesian Methods. The series aims to provide an up-to-date overview of the why?, how? and what? of Bayesian statistics. This volume provides a thorough account of key basic concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development, which provides, in particular, a detailed treatment of the problem of specification of so-called "prior ignorance". The work is written from the authors’ committed Bayesian perspective, but an overview of non-Bayesian theories is provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material should be accessible to readers with a knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics. Contents Preface Chapter 1 Introduction Chapter 2 Foundations Chapter 3 Generalisations Chapter 4 Modelling Chapter 5 Inference Chapter 6 Remodelling Appendix A Summary of Basic Formulae Appendix B Non-Bayesian Theories References Subject index Author Index
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