Think Bayes
Bayesian Statistics in Python
by Allen Downey
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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 book'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. 
Book Description
If you know how to program with Python and also know a little about probability, you'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 you'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 book'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.
This open book is licensed under a Creative Commons License (CC BY-NC). You can download Think Bayes ebook for free in PDF format (2.9 MB).
Table of Contents
Chapter 1
Bayess Theorem
Chapter 2
Computational Statistics
Chapter 3
Estimation
Chapter 4
More Estimation
Chapter 5
Odds and Addends
Chapter 6
Decision Analysis
Chapter 7
Prediction
Chapter 8
Observer Bias
Chapter 10
Approximate Bayesian Computation
Chapter 11
Hypothesis Testing
Chapter 12
Evidence
Chapter 13
Simulation
Chapter 14
A Hierarchical Model
Chapter 15
Dealing with Dimensions
Index
About the Author
Book Details
Title
Think Bayes
Subject
Computer Science
Publisher
O'Reilly Media, Green Tea Press
Published
2013
Pages
213
Edition
1
Language
English
ISBN13
9781491945438
ISBN10
1491945435
ISBN13 Digital
9781449370787
ISBN10 Digital
1449370780
PDF Size
2.9 MB
License
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