Preface -- 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

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.

Attribution-NonCommercial

In English.

Description based on online resource