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Think Bayes, by Allen B. Downey
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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.
- Sales Rank: #74578 in Books
- Published on: 2013-10-04
- Released on: 2013-10-04
- Original language: English
- Number of items: 1
- Dimensions: 9.19" h x .41" w x 7.00" l, .77 pounds
- Binding: Paperback
- 214 pages
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.
Most helpful customer reviews
108 of 109 people found the following review helpful.
'Stick' or 'Switch'?
By Ricardo Dapaz
Should you buy this book given that the only other review as of this time is a negative review (based on the lack of a table of contents)? Hmm, that is exactly the sort of decision analysis that is covered by this book. Should you wait for the next train or catch a taxi instead? Or what about the classic Monty Hall problem where there is car hidden behind one of three doors in a TV game show? The contestant picks a door, but prior to opening it, the host opens another door which does not contain the car and then offers the contestant the opportunity to 'stick' to his current selection or 'switch' to the other door. Should the contestant 'stick' or 'switch'? Bayes's Theorem provides a rationale for making this decision and this book covers all of this and more.
This is a great book and a good introduction to the application of Bayes's Theorem in a number of scenarios. The theoretical aspects are well accessible and the Python code is sufficiently clear. This is not an introduction to Python and readers should be relatively familiar with Python or other high level languages to make the most out of this book.
The PDF for the book is freely available from Green Tea Press. If you are concerned about the lack of a table of contents in the mobi version, get the paper copy until this is resolved... I would highly recommend it.
20 of 21 people found the following review helpful.
Really Helpful Book
By MT Moses
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.
15 of 16 people found the following review helpful.
Great problems, but explanation style could be better
By Pavlo P.
The book has really interesting problems and solutions to them, but since solutions are given in Python code form, it is really hard to comprehend new concepts sometimes. It would be much better if the author explain it via simpler example (and sometimes he does) or via pure math and only then jump to the code part. Also this book contains amazing exercises, but unfortunately no solutions to them. The code is well written, but structured poorly - huge files (1800 lines long) are really difficult to examine and learn from.
Overall the book is super interesting, although explanation style could be better.
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