
Principles of Uncertainty
Joseph B. Kadane(Author)
CRC Press
2nd Edition
Published on 26. August 2020
Book
Hardback
524 pages
978-1-138-05273-4 (ISBN)
Description
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
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
Reviews / Votes
"...Overall, my enthusiasm for the book, its original (and of course subjective) defence of the Bayesian approach, and its highly enjoyable style, remains intact! Especially when backed by the proof-in-the pudidng 2017 Pragmatics of Uncertainty..."- Christian P. Robert, CHANCE 2020 'my enthusiasm for the book, its original (and of course, subjective) defense of the Bayesian approach, and its highly enjoyable style, remains intact, especially when backed by the proof-in-the pudding 2017 Pragmatics of Uncertainty, which I reviewed in a 2019 issue of CHANCE (32(1)).'
- Christian Robert (2021) Principles of Uncertainty (Second Edition), CHANCE, 34:1, 54-55, DOI: 10.1080/09332480.2021.1885939
More details
Series
Edition
2nd edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
28 s/w Zeichnungen, 28 s/w Abbildungen, 8 s/w Tabellen
8 Tables, black and white; 28 Line drawings, black and white; 28 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 33 mm
Weight
1173 gr
ISBN-13
978-1-138-05273-4 (9781138052734)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Joseph B. Kadane
Principles of Uncertainty
Book
05/2024
2nd Edition
Chapman & Hall/CRC
€67.80
Shipment within 15-20 days

Joseph B. Kadane
Principles of Uncertainty
E-Book
11/2020
2nd Edition
Chapman & Hall/CRC
€63.49
Available for download

Joseph B. Kadane
Principles of Uncertainty
E-Book
11/2020
2nd Edition
Chapman & Hall/CRC
€63.49
Available for download
Person
Joseph B. Kadane (born January 10, 1941) is the Leonard J. Savage University Professor of Statistics, Emeritus in the Department of Statistics and Social and Decision Sciences at Carnegie Mellon University. He is one of the early proponents of Bayesian statistics, particularly the subjective Bayesian philosophy. Born in Washington, DC and raised in Freeport on Long Island, Kadane (known as Jay), prepared at Phillips Exeter Academy, earned an A.B. in mathematics from Harvard College and a Ph.D. in statistics from Stanford in 1966, under the supervision of Professor Herman Chernoff. While in graduate school, Jay worked for the Center for Naval Analyses (CNA). Upon finishing, he accepted a joint appointment at the Yale statistics department and the Cowles Foundation. In 1968 he left Yale and served as an analyst at CNA for three years. In 1971, Jay moved to Pittsburgh to join Morris H. DeGroot at Carnegie Mellon University. He became the second tenured professor in the Department of Statistics. Jay served as department head from 1972-1981 and steered the department to a balance between theoretical and applied work, advocating that statisticians should engage in joint research in substantive areas rather than acting as consultants.Jay's contributions span a wide range of fields: econometrics, law, medicine, political science, sociology, computer science (see maximum subarray problem), archaeology, and environmental science, among others. Among many honors, Jay has been elected as a Fellow of the American Academy of Arts and Sciences, a fellow of the American Association for the Advancement of Science, a fellow of the American Statistical Association, and a fellow of the Institute of Mathematical Statistics. He has authored over 250 peer-reviewed publications and has served the statistical community in many capacities, including as editor of the Journal of the American Statistical Association from 1983-85.
Content
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'