Introduction
PART I: INTRODUCTION TO PROBABILITY
Chapter 1: Bayesian Thinking and Everyday Reasoning
Chapter 2: Measuring Uncertainty
Chapter 3: The Logic of Uncertainty
Chapter 4: Creating a Binomial Probability Distribution
Chapter 5: The Beta Distribution
PART II: BAYESIAN PROBABILITY AND PRIOR PROBABILITIES
Chapter 6: Conditional Probability
Chapter 7: Bayes’ Theorem with LEGO
Chapter 8: The Prior, Likelihood, and Posterior of Bayes’ Theorem
Chapter 9: Bayesian Priors and Working with Probability Distributions
PART III: PARAMETER ESTIMATION
Chapter 10: Introduction to Averaging and Parameter Estimation
Chapter 11: Measuring the Spread of Our Data
Chapter 12: The Normal Distribution
Chapter 13: Tools of Parameter Estimation: The PDF, CDF, and Quantile Function
Chapter 14: Parameter Estimation with Prior Probabilities
PART IV: HYPOTHESIS TESTING: THE HEART OF STATISTICS
Chapter 15: From Parameter Estimation to Hypothesis Testing: Building a Bayesian A/B Test
Chapter 16: Introduction to the Bayes Factor and Posterior Odds: The Competition of Ideas
Chapter 17: Bayesian Reasoning in the Twilight Zone
Chapter 18: When Data Doesn’t Convince You
Chapter 19: From Hypothesis Testing to Parameter Estimation
Appendix A: A Quick Introduction to R
Appendix B: Enough Calculus to Get By
Index