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Top (max 10) reviews: Think Bayes: Bayesian Statistics in Python.

4.0 out of 5.0    23 total reviews.

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by Ricardo Dapaz on Oct. 4, 2013

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.

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by Amazon Customer on July 8, 2017

No problems

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by Reuben on Jan. 22, 2016

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

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by Justin Elder on March 12, 2014

I've used this to work with my dad on some data processing work. It's helped in my work as well. Great for small projects.

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by Ke (Kevin) Wu on Dec. 1, 2015

Helped me get a job.

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by Dr. Howard B. Bandy on July 7, 2014

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.

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by Theodore D. Sternberg on April 29, 2014

This is a nice book with some really neat examples. What makes it unusual is that where a conventional math stats book would use math notation to help explain the ideas, this book tries to do that with Python code. It's an interesting idea; I imagine the author thought there was a market among engineers comfortable reading code but intimidated by math. So if you're such a person, this could be a really good book for you, just the thing to introduce you to an interesting area of practical...um...math!
On the other hand, if you do relate well to math notation, you might find the computer code a distraction.

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by W. Hogan on Dec. 12, 2013

I wish the book had been written using R. The examples are interesting and the text is concise. I enjoyed reading this book.

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by Pavlo P. on Nov. 7, 2014

The book has really interesting problems and solutions to them, but since solutions are given in Python code form, it is really hard to comprehend new concepts sometimes. It would be much better if the author explain it via simpler example (and sometimes he does) or via pure math and only then jump to the code part. Also this book contains amazing exercises, but unfortunately no solutions to them. The code is well written, but structured poorly - huge files (1800 lines long) are really difficult to examine and learn from.
Overall the book is super interesting, although explanation style could be better.

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by MT Moses on Dec. 15, 2013

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.