- Paperback: 214 pages
- Publisher: O'Reilly Media; 1 edition (October 4, 2013)
- Language: English
- ISBN-10: 1449370780
- ISBN-13: 978-1449370787
- Product Dimensions: 7 x 0.4 x 9.2 inches
- Shipping Weight: 12.6 ounces (View shipping rates and policies)
- Average Customer Review: 23 customer reviews
- Amazon Best Sellers Rank: #163,101 in Books (See Top 100 in Books)
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Think Bayes: Bayesian Statistics in Python 1st Edition
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About the Author
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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This is a great book and a good introduction to the application of Bayes's Theorem in a number of scenarios. The theoretical aspects are well accessible and the Python code is sufficiently clear. This is not an introduction to Python and readers should be relatively familiar with Python or other high level languages to make the most out of this book.
The PDF for the book is freely available from Green Tea Press. If you are concerned about the lack of a table of contents in the mobi version, get the paper copy until this is resolved... I would highly recommend it.
In this book, he gives a clear introduction to Bayesian analysis using well through out examples and Python code. There is a small amount of math. He makes very effective use of probability density functions, cumulative distribution functions, and simulations.
He provides multiple examples of model development, including design, testing, and analysis.
The book is appropriate and effective for self study. Highly recommended.
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Likelihood p(D|H), author "scared me a little" describing and explaining it.Read more