Praise for the first edition:

Principles of Uncertainty

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

Praise for the first edition:

Principles of Uncertainty

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

Praise for the first edition:

Principles of Uncertainty

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