Principles of uncertainty
著者
書誌事項
Principles of uncertainty
（Texts in statistical science）
CRC Press, 2020, c2021
2nd ed
- : hbk
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注記
"A Chapman & Hall book"
Bibliography: p. 463-480
Includes indexes
内容説明・目次
内容説明
Praise for the first edition:
Principles of Uncertainty
is a profound and mesmerising book on the foundations and principles of subjectivist or behaviouristic Bayesian analysis. ... the book is a pleasure to read. And highly recommended for teaching as it can be used at many different levels. ... A must-read for sure!-Christian Robert, CHANCEIt's a lovely book, one that I hope will be widely adopted as a course textbook.
-Michael Jordan, University of California, Berkeley, USA
Like the prize-winning first edition, Principles of Uncertainty, Second Edition is an accessible, comprehensive text on the theory of Bayesian Statistics written in an appealing, inviting style, and packed with interesting examples. It presents an introduction to the subjective Bayesian approach which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods. This new edition has been updated throughout and features new material on Nonparametric Bayesian Methods, the Dirichlet distribution, a simple proof of the central limit theorem, and new problems.
Key Features:
First edition won the 2011 DeGroot Prize
Well-written introduction to theory of Bayesian statistics
Each of the introductory chapters begins by introducing one new concept or assumption
Uses "just-in-time mathematics"-the introduction to mathematical ideas just before they are applied
目次
Probability. Conditional Probability and Bayes Theorem. Discrete Random Variables. Probability generating functions. Continuous Random Variables. Transformations. Normal Distribution. Making Decisions. Conjugate Analysis. Hierarchical Structuring of a Model. Markov Chain Monte Carlo. Multiparty Problems. Exploration of Old Ideas. Nonparametric Bayesian Methods. Epilogue: Applications'
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