Free Ebook Think Bayes: Bayesian Statistics in Python

Free Ebook Think Bayes: Bayesian Statistics in Python

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Think Bayes: Bayesian Statistics in Python

Free Ebook Think Bayes: Bayesian Statistics in Python

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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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Product details

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:

4.0 out of 5 stars

25 customer reviews

Amazon Best Sellers Rank:

#70,019 in Books (See Top 100 in Books)

Should you buy this book given that the only other review as of this time is a negative review (based on the lack of a table of contents)? Hmm, that is exactly the sort of decision analysis that is covered by this book. Should you wait for the next train or catch a taxi instead? Or what about the classic Monty Hall problem where there is car hidden behind one of three doors in a TV game show? The contestant picks a door, but prior to opening it, the host opens another door which does not contain the car and then offers the contestant the opportunity to 'stick' to his current selection or 'switch' to the other door. Should the contestant 'stick' or 'switch'? Bayes's Theorem provides a rationale for making this decision and this book covers all of this and more.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.

This is one of several introductory level books written by Dr. Downey recently. All of them are excellent.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.

This book is really great in the regards of the concept it teaches and the examples it displays them in. A good book if you are interested in Data Science from a technical aspect, but do not have a strong statistical understanding. What I did not like about the book is that the code is outdated so be prepared to be looking for fixes to the code

Great book to simplify the Bayes process. It goes into basic detail as a real how-to. This is not an academic text but a book to teach how to use Bayes for everyday problems.

I like the chance to follow the examples with the help of the website for data.It might be nice to provide solutions using R

The book is pretty good in explaining the basic idea behind Bayesian approach. You must know some probability theory to understand it. However, with more complicated examples, the author suggests his Python code instead of explanation, and ask us not to worry, because the code (which we can download if we want) is working.

Great book, the sample code is easy to use. Only complaint is that the code is python 2.7 compliant and not 3.x

It isn't a deep treatment of the subject but it gives working examples to help with basic ideas. Not a production ready line of code for serious work but useful.

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Think Bayes: Bayesian Statistics in Python PDF

Think Bayes: Bayesian Statistics in Python PDF