Think Bayes: Bayesian Statistics Made Simple

(1 review)


Allen Downey, Franklin W. Olin College of Engineering

Pub Date: 2012

ISBN 13: 978-1-4493707-8-7

Publisher: Green Tea Press

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Reviewed by Edwin Chong, Professor, Colorado State University, on 12/6/2016.

The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods.… read more


Table of Contents

1 Bayes’s Theorem
2 Computational Statistics
3 Estimation
4 More Estimation
5 Odds and Addends
6 Decision Analysis
7 Prediction
8 Observer Bias
9 Two Dimensions
10 Approximate Bayesian Computation
11 Hypothesis Testing
12 Evidence
13 Simulation
14 A Hierarchical Model
15 Dealing with Dimensions

About the Book

Think Bayes is an introduction to Bayesian statistics using computational methods.

The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.

I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.

About the Contributors


Allen B. Downey is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc.