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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 programming skills to learn and understand Bayesian statistics
Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing
Get started with simple examples, using coins, dice, and a bowl of cookies
Learn computational methods for solving real-world problems
About the Author
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.
Product details
Publisher : O'Reilly Media; 2nd edition (June 8, 2021)
Language : English
Paperback : 338 pages
ISBN-10 : 149208946X
ISBN-13 : 978-1492089469
Item Weight : 1.19 pounds
Dimensions : 7 x 0.7 x 9.19 inches
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