Conditions of Use
The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. read more
The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods.
Generally, the book's coverage is accurate. Because the style of the book is somewhat informal, sometimes there is some lack of precision (but nothing serious).
The approach is currently very relevant. It uses Python code throughout. I expect Python to continue to be of interest in the near and probably medium-term future, so the longevity of the book should be quite good.
The writing style is somewhat informal, and so clarity will be high for newbies. For those wanting a more formal treatment, it would be better to look for a more advanced, mathematical treatment.
The entire book is written by a single author, using consistent style, terminology, and approach throughout.
After getting the basics down, it should be easy for the reader to read individual chapters and get the key ideas without needing to refer much to other chapters.
The organization and flow is excellent. I found it very easy to read the book cover to cover.
The interface is pleasant and easy to navigate. The Python code scattered throughout the book is also easy to identify and follow.
I did not find any significant grammatical issues.
There are no particular cultural issues that I could see.
Table of Contents
- 1 Bayes's Theorem
- 2 Computational Statistics
- 3 Estimation
- 4 More Estimation
- 5 Odds and Addends
- 6 Decision Analysis
- 7 Prediction
- 8 Observer Bias
- 9 Two Dimensions
- 10 Approximate Bayesian Computation
- 11 Hypothesis Testing
- 12 Evidence
- 13 Simulation
- 14 A Hierarchical Model
- 15 Dealing with Dimensions
About the Book
Think Bayes is an introduction to Bayesian statistics using computational methods.
The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.
Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.
I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
About the Contributors
Allen B. Downey is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc.