Think Bayes

If you know how to program, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you’ll begin to apply these techniques to real-world problems.

Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book’s computational approach helps you get a solid start.

Use your programming skills to learn and understand Bayesian statisticsWork with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testingGet started with simple examples, using coins, dice, and a bowl of cookiesLearn computational methods for solving real-world problems

Author Biography

Allen Downey is a Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master’s and Bachelor’s degrees from MIT. He is author of Think Python, Think Bayes, Think DSP, and a blog, Probably Overthinking It.