Think Bayes: Bayesian Statistics in Python (Allen B. Downey)

 
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Think Bayes: Bayesian Statistics in Python (Allen B. Downey)

You are prepared to tackle Bayesian statistics if you are a programmer. This book teaches you how to use discrete probability distributions rather than continuous mathematics to solve statistical problems rather than using mathematical formulas.

The foundations of Bayesian theory will be clearer once the math is out of the way, and you can then start using these methods to solve actual situations.

Although bayesian statistical approaches are widely used and increasingly significant, there aren't many resources available to aid newcomers. The computational methodology of this book, which is based on college courses written by author Allen B. Downey, gives you a strong foundation.

  • 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, dice, a bowl of cookies, 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.

Ebook Details

About the Authors
Computer science professor Allen Downey teaches at the Olin College of Engineering. At U.C. Berkeley, Colby College, and Wellesley College, he has taught computer science. He graduated from MIT with a Master's and a Bachelor's degree and earned a Ph.D. in computer science from U.C. Berkeley. Think Python, Think Bayes, Think DSP, and a blog called Probably Overthinking It are all written by him.
Published
Published Date / Year
2nd edition (June 15, 2021); eBook (CC Edition by Green Tea Press)
License(s)
CC BY-NC 4.0
Hardcover
338 pages
eBook Format
HTML
ISBN-10
149208946X
ISBN-13
978-1492089469

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