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Think Bayes: Bayesian Statistics in Python 1st Edition

by Allen Downey (Author)

4.2 100 ratings

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Think Bayes: Bayesian Statistics in Python (O'reilly)
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If you know how to program with Python and also know a little about probability, youâ??re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youâ??ll begin to apply these techniques to real-world problems.

Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this bookâ??s computational approach helps you get a solid start.

Use your existing programming skills to learn and understand Bayesian statistics
Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing
Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey
Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

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Editorial Reviews

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.

Product details

Publisher ‏ : ‎ O'Reilly Media; 1st edition (October 29, 2013)
Language ‏ : ‎ English
Paperback ‏ : ‎ 211 pages
ISBN-10 ‏ : ‎ 1449370780
ISBN-13 ‏ : ‎ 978-1449370787
Item Weight ‏ : ‎ 12.5 ounces
Dimensions ‏ : ‎ 7 x 0.41 x 9.19 inches

Best Sellers Rank: #1,463,964 in Books (See Top 100 in Books)
- #901 in Data Processing
- #1,563 in Python Programming
- #2,509 in Probability & Statistics (Books)

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4.2 100 ratings

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Allen Downey

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Allen Downey is a Professor of Computer Science at Olin College and author of books related to programming and data science, including Think Python, Think Stats, Think Bayes, Think Complexity, and several others. The idea behind these books is that if you know how to program, you can use that skill to learn other things.

Allen has a Ph.D. from U.C. Berkeley and M.S. and B.S. degrees from MIT. He has taught at Colby College and Wellesley College.

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Dr. Howard B. Bandy

An excellent introduction to Bayesian analysis

Reviewed in the United States on July 7, 2014

Verified Purchase

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.

21 people found this helpful

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MT Moses

Really Helpful Book

Reviewed in the United States on December 16, 2013

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.

27 people found this helpful

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rpm507

Reading this as part of preparation for Data Science studies

Reviewed in the United States on December 22, 2013

Verified Purchase

I am finishing a Masters in a different area but Data Science is going to be my next area of study. Keep seeing Bayesian in my preparatory reading so I thought an introductory text must be included. So far this book has been very helpful but remember I am just trying to build a set of basic knowledge. A review from someone with more knowledge of Data Science would probably be of greater use.

5 people found this helpful

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Michael

overpriced

Reviewed in the United States on February 24, 2014

Verified Purchase

The content is good, but it's available for free on the authors webpage in pdf and html. Not sure why this version exists. Though I bought a copy before i looked at the site so that is probably why.

6 people found this helpful

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seapwc

Great Book written by an accomplished instructor.

Reviewed in the United States on November 30, 2018

Verified Purchase

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

One person found this helpful

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Anonymous123

Great book, the sample code is easy to use

Reviewed in the United States on January 22, 2016

Verified Purchase

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

9 people found this helpful

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Ssss

No Table of Contents. Very inconvenient.

Reviewed in the United States on September 23, 2013

Verified Purchase

I bought the ebook and realized there's no table of content. Very inconvenient. I returned the book right away. This rating is mainly for the lack of TOC. When author published the TOC in the ebook, I'll buy again because the book is an interesting topic to me.

11 people found this helpful

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W. Hogan

Don't let Python get in the way

Reviewed in the United States on December 12, 2013

Verified Purchase

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

8 people found this helpful

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Top reviews from other countries

John W. Middelveen

A Bayes 'How tp Use.'

Reviewed in Canada on February 19, 2021

Verified Purchase

This is a good way to learn and how to, both Python and Bayes. I willhave to get his Python. I like the style.

Eli Emanuel

Three related ideas: (1) Bayesian Thinking, (2) Computational Approach, & (3) Iterative Modelling.

Reviewed in Singapore on May 3, 2021

Verified Purchase

I really enjoyed this book. It was a light read. There are ample examples of which Bayes theorem, Bayesian thinking, probability and statistics were elucidated. The reading will get a glimpse of Bayesian probability from other sources such as: other books, or webpages. The chapters are short and sweet and there is substantial effort made by the author to explain the workings of the codes. On top of that, the author have made notable effort to make this book engaging and interesting.

The mathematics, and probabilistic concepts in this book are comprehensible and the author have a way to make abstruse concepts readily understandable. The online resources for this book is a well companion to the physical copy.

This book has many exercises as well as discussions (mostly based on the chapters). I finished reading this book in an hour and would reread it a few more times to benefit most of this book, as well as going through the lines of code on the websites listed in this book line-by-line. I believe any reader would definitely benefit from reading this book. I would definitely recommend this book to people interested in: Data Science, Machine Learning, Mathematics, Statistics, and Probability.

sunil

amazon.in is a pirated copy

Reviewed in India on July 18, 2016

Verified Purchase

That copy that i got from amazon.in is a pirated copy and poor in quality

4 people found this helpful

Mr. C Shearwood

Nice idea, poor execution, even worse code.

Reviewed in the United Kingdom on December 22, 2015

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.

12 people found this helpful

R. Tillaart

Good introductionary book about implementing bayesian logic in python

Reviewed in Germany on March 7, 2014

Verified Purchase

Allen Downey has written several books and this is one I use as a reference as it explains the bayesian logic very well.
For those not familiar or not using with python, the book might be less valuable (but would still get 4 stars :)

The reader should have reasonable knowledge of math/statistics.

4 people found this helpful

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