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- Title Think Bayes: Bayesian Statistics Made Simple
- Author(s) Allen B. Downey
- Publisher: Green tea Press, 2012 (draft)
- Paperback N/A
- eBook HTML and PDF (77 pages, 323 KB)
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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Think Bayes is an introduction to Bayesian statistics using computational methods and Python programming language. Bayesian statistics are usually presented mathematically, but many of the ideas are easier to understand computationally. Contents: Bayes's Theorem; Computational statistics; Tanks and Trains; Urns and Coins; Odds and addends; Hockey; The variability hypothesis; Hypothesis testing.
The fundamental idea behind all Bayesian statistics is Bayes’s Theorem, which is surprisingly easy to derive, provided that you understand conditional probability. So we’ll start with probability, then conditional probabilty, then Bayes’s Theorem, and on to Bayesian statistics.
This book uses material from O'Reilly® Think Stats: Probability and Statistics for Programmers ©2011 (Allen B. Downey). People who know some Python have a head start.
About the Authors- Allen Downey is an Associate 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.
