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Think Bayes Paperback – 4 Oct 2013

3.0 out of 5 stars 2 customer reviews

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Product details

  • Paperback: 210 pages
  • Publisher: O'Reilly Media; 1 edition (4 Oct. 2013)
  • Language: English
  • ISBN-10: 1449370780
  • ISBN-13: 978-1449370787
  • Product Dimensions: 17.8 x 1 x 23.3 cm
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 410,484 in Books (See Top 100 in Books)

Product Description

Book Description

Bayesian Statistics in Python

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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Format: Paperback Verified Purchase
The book explains a number of problems that can be solved with Bayesian statistics, and presents code using a framework the author has written that solves the problem. However, the author does not explain many of the problems very well and the code they have written is not written in a pythonic style. The author themselves admits that the code does not conform to the language's style guide and instead conforms to the Google style guide (as they were working their during the beginning of the work on the book) but I feel this shows a lack of care on their part. I think I spent more time gritting my teeth at the poor code than actually interrogating the samples. A lack of documentation for the framework seriously hampers the code samples as well.

Whilst I understand that printing in colour would have been more expensive, syntax highlighting (even if just in the PDF) would have been greatly appreciated. The code samples could have, regardless, have been better typeset to differentiate them from the prose.
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Format: Kindle Edition
Decent cookbook of bayesian stats examples.
The book disposes of Bayes in the first chapter and the rest of the book is examples which are well thought out.
However the difficulty is with the python examples - the code is all in Python 2 so if you run it in the latest version of Python 3 you will gets lots of silly errors which are a pain to debug, also you need to download lots of modules if you are running the code on windows - the go to site is Christoph Gohlke's Unofficial Windows Binaries for Python Extension Packages.
It takes three or four downloads per example to get things working.
Having said that this is my first go with Python and I quite liked it.

So overall the examples need to be reworked, but otherwise quite entertaining.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: HASH(0x8efab168) out of 5 stars 21 reviews
107 of 108 people found the following review helpful
HASH(0x8ef5e4e0) out of 5 stars 'Stick' or 'Switch'? 5 Oct. 2013
By Ricardo Dapaz - Published on Amazon.com
Format: Paperback
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.
20 of 20 people found the following review helpful
HASH(0x8ef5e21c) out of 5 stars Really Helpful Book 16 Dec. 2013
By MT Moses - Published on Amazon.com
Format: Paperback Verified Purchase
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.
13 of 14 people found the following review helpful
HASH(0x8f9a3a68) out of 5 stars Great problems, but explanation style could be better 7 Nov. 2014
By Pavlo P. - Published on Amazon.com
Format: Paperback
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.
16 of 18 people found the following review helpful
HASH(0x8ee30b40) out of 5 stars Great Introduction for this Topic 22 Oct. 2013
By Michael Morgan - Published on Amazon.com
Format: Paperback
I used the greenteapress version to teach an intro class on Bayes before this paper version was released. It worked great (but PayPal rejected my donation). Straightforward understandable examples allowed me to keep more complicated examples in context. This in conjunction with CamDavidsonPilon's excellent community book really gave a well-rounded class.
8 of 8 people found the following review helpful
HASH(0x8ee494ec) out of 5 stars Novel approach 30 April 2014
By Theodore D. Sternberg - Published on Amazon.com
Format: Paperback
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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