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.
Table of Contents
Chapter 1. Bayes’s 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 9. Two Dimensions 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
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