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 Title Think Bayes: Bayesian Statistics in Python
 Author(s) Allen B. Downey
 Publisher: O'Reilly Media (October 1, 2013); Green tea Press, 2012 (Version 1.01)
 License(s): CC BYNC 4.0
 Paperback 210 pages
 eBook HTML and PDF (213 pages, 2.3 MB)
 Language: English
 ISBN10: 1449370780
 ISBN13: 9781449370787
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Book Description
If you know how to program with Python and also know a little about probability, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of 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 realworld 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 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, M&Ms, Dungeons & Dragons dice, paintball, and hockey
 Learn computational methods for solving realworld problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
 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.
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