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Think Bayes

3.85  ·  Rating details ·  207 ratings  ·  12 reviews
Kindle Edition, 210 pages
Published September 12th 2013 by O'Reilly Media (first published January 1st 2012)
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Average rating 3.85  · 
Rating details
 ·  207 ratings  ·  12 reviews

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Dec 29, 2015 rated it really liked it
Science has been described as simply “a collection of successful recipes”. In “Think Bayes” Allen B. Downey has attempted just that by presenting a set of instructional tutorials for teaching bayesian methods with Python. In essence it’s an instructional book with examples that are meant to be straightforward by giving you a simple set of rules in solving more complex sets of problems. The book also makes a few style choices, ignoring continuous distributions in an effort to focus on discrete di ...more
Augusto Barros
Jan 14, 2014 rated it really liked it
very good Bayesian introduction, specially because it's light on mathematics and full of practical content. I searched for this kind of content for a long time, but was surprised to find in a book like this.
Mar 02, 2016 rated it liked it  ·  review of another edition
This book is great in term of providing wide range of examples and exercises by those we can understand more about how to "think bayes". However, there are still lacks of detail explaination , and mixture of python code and math is not making it easier to understand.
May 24, 2017 rated it really liked it  ·  review of another edition
While the methodology behind the framework of the code examples wasn't always obvious (and seemed occasionally overwrought), I think the core statistical concepts come through clearly enough that they could be reimplemented in whatever fashion made most sense to the reader. Generally fairly concise, and generous with graphical outputs as well, which helped solidify conceptual aspects of distributions and their properties.
Sep 09, 2019 rated it liked it
Shelves: probability
Like the applied ethos and no-nonsense fun example problems; a somewhat casual style is also refreshing.
However I think the author went overboard; using Python is redundant and simplistic; and a few mathematical expressions would not have hurt anyone.
Mike Peleah
Jul 17, 2019 rated it really liked it  ·  review of another edition
Good and practical introduction into Bayesian Statistics using Python. While it won't really teach you how to think Bayes, it offers a number of good and practical examples with good discussion.
Abhilash Gopalakrishnan
May 15, 2020 rated it it was amazing
Great book which initiatives us into bayes in models and thinking.
Sergey Shishkin
Jun 18, 2016 rated it liked it
Good introductory book with interesting example problems. The example code layers abstractions on top of the previously introduced ones from chapter to chapter. Over time it gets hard to comprehend the examples due to class-based polymorphism with multiple levels of inheritance. If not this annoyance, it would be a great book.
Matthew Talbert
Mar 31, 2015 rated it liked it
Good introduction to Bayesian analysis. I didn't take the time this time through to do all of the code samples and exercises, but I still got a decent overview. One of the best parts was the first really good explanation of the Monty Hall problem that I've seen; I finally understand it!
Dec 20, 2013 rated it it was amazing
Shelves: programming
an easy to follow bayesian guide
Jun 09, 2014 added it
Shelves: deferred
I'm not giving this up because I didn't find it interesting. I'm putting it on hold because there are some technical books that I need to read first (for work purposes.)
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Allen Downey is a professor of Computer Science at Olin College and the author of a series of open-source textbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. in computer science from U.C. Be ...more

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