Principles of Uncertainty

2011-05-18 | ISBN: 1439861617 | 503 pages | PDF | 2,94 MB

An intuitive and mathematical introduction to subjective probability and Bayesian statistics.

An accessible, comprehensive guide to the theory of Bayesian statistics, Principles of Uncertainty presents the subjective Bayesian approach, which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods. Both rigorous and friendly, the book contains:

-Introductory chapters examining each new concept or assumption
-Just-in-time mathematics ¨C the presentation of ideas just before they are applied
-Summary and exercises at the end of each chapter
-Discussion of maximization of expected utility
-The basics of Markov Chain Monte Carlo computing techniques
-Problems involving more than one decision-maker

Written in an appealing, inviting style, and packed with interesting examples, Principles of Uncertainty introduces the most compelling parts of mathematics, computing, and philosophy as they bear on statistics. Although many books present the computation of a variety of statistics and algorithms while barely skimming the philosophical ramifications of subjective probability, this book takes a different tack. By addressing how to think about uncertainty, this book gives readers the intuition and understanding required to choose a particular method for a particular purpose.

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!
¡ªChristian Robert, The Statistics Forum/CHANCE, October 2011

It's a lovely book, one that I hope will be widely adopted as a course textbook.
¡ªMichael Jordan, University of California, Berkeley, USA

A careful, complete, and lovingly written exposition of the subjective Bayesian viewpoint by one of its most eloquent and staunch defenders. Summarizes a lifetime of theory, methods, and application developments for the Bayesian inferential engine. A must-read for anyone looking for a deep understanding of the foundations of Bayesian methods and what they offer modern statistical practice.
¡ªBradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA

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