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O'Reilly® Think Bayes: Bayesian Statistics in Python
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  • Title: Think Bayes: Bayesian Statistics in Python
  • Author(s) Allen B. Downey
  • Publisher: O'Reilly Media; 2nd edition (June 15, 2021); eBook (CC Edition by Green Tea Press)
  • License(s): CC BY-NC 4.0
  • Paperback: 338 pages
  • eBook: HTML, PDF, ePub, Kindle, etc.
  • Language: English
  • ISBN-10: 149208946X
  • ISBN-13: 978-1492089469
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Book Description

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 there aren't many resources 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 existing programming skills to learn and understand Bayesian statistics
  • Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing
  • Get started with simple examples, using coins, dice, and a bowl of cookies, M&Ms, Dungeons & Dragons dice, paintball, and hockey
  • Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
About the Authors
  • Allen Downey is a Professor of Computer Science at 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 the author of Think Python, Think Bayes, Think DSP, and a blog, Probably Overthinking It.
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