Bayesian Statistics Made Simple
by Allen B. Downey
The second edition of Think Bayes is in progress. The first ten chapters are available now as an early release.
The first edition is still available here:
The code for this book is in this GitHub repository.
Or if you are using Python 3, you can use this updated code.
Roger Labbe has transformed Think Bayes into Jupyter notebooks where you can modify and run the code.
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 functions. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are loops or array operations.
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.
Think Bayes is a Free Book, which means that you are free to copy, distribute, and modify it, as long as you attribute the work, share alike, and don’t use it for commercial purposes.
Other Free Books by Allen Downey are available from Green Tea Press.