## Book description

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than 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 there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start.

- Use your programming skills to learn and understand Bayesian statistics
- Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing
- Get started with simple examples, using coins, dice, and a bowl of cookies
- Learn computational methods for solving real-world problems

## Table of contents

- Preface
- 1. Probability
- 2. Bayes’s Theorem
- 3. Distributions
- 4. Estimating Proportions
- 5. Estimating Counts
- 6. Odds and Addends
- 7. Minimum, Maximum, and Mixture
- 8. Poisson Processes
- 9. Decision Analysis
- 10. Testing
- 11. Comparison
- 12. Classification
- 13. Inference
- 14. Survival Analysis
- 15. Mark and Recapture
- 16. Logistic Regression
- 17. Regression
- 18. Conjugate Priors
- 19. MCMC
- 20. Approximate Bayesian Computation
- Index

## Product information

- Title: Think Bayes, 2nd Edition
- Author(s):
- Release date: May 2021
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492089469

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