Principles of Uncertainty
Principles of Uncertainty by Joseph B Kadane is a comprehensive text on Bayesian analysis, published by Taylor & Francis Limited in May 2024. This second edition spans 498 pages and is presented in English. The book offers an accessible introduction to the subjective Bayesian approach, which is significant in fields such as game theory and economics, and includes updated content on Nonparametric Bayesian Methods and the Dirichlet distribution.
Readers will find a well-structured exploration of Bayesian statistics, featuring engaging examples and a clear presentation of concepts. The text introduces new ideas progressively, employing a “just-in-time mathematics” approach to ensure that mathematical concepts are presented right before their application. This edition also includes new problems and a simple proof of the central limit theorem, making it suitable for various educational levels in the realms of science and mathematics.
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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, CHANCE
It’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
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