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Principles of Uncertainty (Chapman & Hall/CRC Texts in Statistical Science) Hardcover – May 18, 2011

ISBN-13: 978-1439861615 ISBN-10: 1439861617 Edition: 1st

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Editorial Reviews

Review

This book mainly focuses on the use of Bayesian statistics. It is written using stories and many examples to which readers can relate, and is thus an engaging and appealing text on what is generally a very dry mathematical subject.
—John J. Shea, IEEE Electrical Insulation Magazine, May/June 2013, Vol. 29, No. 3

This text provides a unique blend of theory, methods, philosophy and applications that is suitable for a course in Bayesian probability and statistics. … provides thought-provoking material for teaching. …
—Erkki P. Liski, International Statistical Review, 2012

In this remarkable book, Kadane begins at the most rudimentary level, develops all the needed mathematics on the fly, and still manages to flesh out at least the core of the whole story, slowly, thoughtfully, and rigorously, right up to graduate level. Major theorems all proved in detail appear here, but not for their own sake; the author always carefully selects them to clarify the basic meaning of the subject and his own views concerning the pitfalls and subtleties of its proper application. Summing Up: Highly recommended.
—D.V. Feldman, CHOICE, February 2012

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, The Statistics Forum/CHANCE, October 2011

It's a lovely book, one that I hope will be widely adopted as a course textbook.
—Michael 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.
—Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA

About the Author

Joseph B. Kadane, Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA


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