Goodreads helps you keep track of books you want to read.
Start by marking “Think Bayes” as Want to Read:
Think Bayes
Enlarge cover
Rate this book
Clear rating
Open Preview

Think Bayes

by
3.83  ·  Rating details ·  182 ratings  ·  11 reviews
Kindle Edition, 210 pages
Published September 12th 2013 by O'Reilly Media (first published January 1st 2012)
More Details... Edit Details

Friend Reviews

To see what your friends thought of this book, please sign up.

Reader Q&A

To ask other readers questions about Think Bayes, please sign up.

Be the first to ask a question about Think Bayes

Community Reviews

Showing 1-30
Average rating 3.83  · 
Rating details
 ·  182 ratings  ·  11 reviews


More filters
 | 
Sort order
Start your review of Think Bayes
Jake
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 ...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.
huydx
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.
Alex
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.
Daniel
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.
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!
Dgg32
Dec 20, 2013 rated it it was amazing
Shelves: programming
an easy to follow bayesian guide
Daniel
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.)
Artur
rated it really liked it
Feb 19, 2015
Ashutosh Kanungo
rated it it was amazing
Feb 12, 2018
Eche
rated it it was amazing
Mar 08, 2016
Phil Moyer
rated it really liked it
Jan 10, 2016
Michael Dreimiller
rated it really liked it
Jan 03, 2017
Jan
rated it really liked it
Apr 14, 2017
Jon Nickerson
rated it it was amazing
Aug 05, 2019
Lachlan Phillips
rated it it was amazing
Nov 18, 2015
Pavithra
rated it really liked it
Dec 28, 2017
Kevin Miu
rated it liked it
Oct 03, 2014
Eric Ness
rated it it was amazing
Jan 03, 2019
Ryan Little
rated it it was amazing
Aug 04, 2015
Mind Master
rated it it was amazing
Sep 08, 2019
Diego Essaya
rated it liked it
Sep 05, 2014
Anna
rated it it was amazing
May 12, 2019
Justin Grimes
rated it really liked it
Oct 19, 2012
William
rated it really liked it
Jun 27, 2019
John
rated it really liked it
Dec 16, 2017
Martin Winkel
rated it really liked it
Jul 31, 2018
« previous 1 3 4 5 6 7 next »
There are no discussion topics on this book yet. Be the first to start one »

Readers also enjoyed

  • Python Data Science Handbook: Tools and Techniques for Developers
  • Python Machine Learning
  • Doing Bayesian Data Analysis: A Tutorial Introduction with R
  • Climate Change: What Everyone Needs to Know
  • Information Theory and Evolution
  • The Art of Statistics: How to Learn from Data
  • Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
  • Statistics Done Wrong: The Woefully Complete Guide
  • Crucible (Sigma Force, #14)
  • Unnatural Causes
  • Bayesian Models: A Statistical Primer for Ecologists
  • Effective C++: 55 Specific Ways to Improve Your Programs and Designs
  • Java 8 Lambdas: Pragmatic Functional Programming
  • Code Simplicity: The Fundamentals of Software
  • Learning Java
  • Core Java, Volume II--Advanced Features
  • Clojure Programming
  • Java Generics and Collections: Speed Up the Java Development Process
See similar books…

Goodreads is hiring!

If you like books and love to build cool products, we may be looking for you.
Learn more »
103 followers
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. ...more
No trivia or quizzes yet. Add some now »