Think Bayes: Bayesian Statistics in PythonIf 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.
|
Contents
Chapter 1 Bayess Theorem | 1 |
Chapter 2 Computational Statistics | 11 |
Chapter 3 Estimation | 19 |
Chapter 4 More Estimation | 31 |
Chapter 5 Odds and Addends | 41 |
Chapter 6 Decision Analysis | 53 |
Chapter 7 Prediction | 67 |
Chapter 8 Observer Bias | 79 |
Chapter 10 Approximate Bayesian Computation | 107 |
Chapter 11 Hypothesis Testing | 123 |
Chapter 12 Evidence | 129 |
Chapter 13 Simulation | 143 |
Chapter 14 A Hierarchical Model | 157 |
Chapter 15 Dealing with Dimensions | 165 |
| 191 | |
About the Author | 195 |
Other editions - View all
Common terms and phrases
Alice and Bob alpha analysis answer arrive Bayes factor Bayes’s theorem Bayesian beta distribution Bowl chance chapter choose coin compute the distribution compute the likelihood compute the probability Conditional distributions cookie problem credible interval dataset def __init__(self def Likelihood(self def Update(self Detector dice diff Dirichlet distribution distribution of goals Dungeons & Dragons efficacy error estimate example Figure Gaussian given guess Here’s the code hypo hypothesis joint distribution likelihood function locomotive problem mean median meta-Pmf modeling decisions Monty Hall problem num_new number of passengers odds p_correct pairs parameters percentiles Player Poisson Poisson process posterior distribution predictive distributions prevalences prior distribution prob provides Python question random range raw scores represents return pmf sample showcase sigma simulations standard deviation statistics Suite test-taker thinkbayes.Pmf tion tumor unseen species update vanilla cookie variable wait