Bayes' Rule with Python: A Tutorial Introduction to Bayesian Analysis

by James V. Stone

Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this book includes Python (3.5) code snippets, which reproduce key numerical results and diagram

176 pages, Paperback

Published April 20, 2016

About the author

Honorary Associate Professor, University of Sheffield, England.

Reviews

[Randy · Rating 4 out of 5]

I needed this book to jump start my study of probabilistic graph models. Up to this book, I had a basic grasp of the basic ideas of priors and Bayes' Rule (written as an equation), but seeing both verbal explanations of equations and then seeing the ideas expressed as small bits of Python, was helpful. I actually owned the non-language version of this book for a long time, but it sat on the shelf until I saw this version and gave the other one away (they are the same book with only the programming bits added to this one).

I've moved on to the full PGM work now, so this book has served it's purpose and I cannot fault it for not having more of the basic information I need - since that is specific to PGMs.

If you want a good introduction to the theory with nice examples, this book does the trick. If you want to see how to implement these ideas from more of a developer/software engineering perspective, I would recommend Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. The two books are both short and complement each other: the first will help you get your head around the theory, the second will move you from snips of code which just translate the equations, to using modern libraries if your implementation will be in Python.