Overview

Virtually everything in life is, to some extent, uncertain. This may seem like a bit of an exaggeration, but to see the truth of it you can try a quick experiment. At the start of the day, write down something you think will hap- pen in the next half-hour, hour, three hours, and six hours. Then see how many of these things happen exactly like you imagined. You’ll quickly realize that your day is full of uncertainties. Even something as predictable as “I will brush my teeth” or “I’ll have a cup of coffee” may not, for some reason or another, happen as you expect.

For most of the uncertainties in life, we’re able to get by quite well by planning our day. For example, even though traffic might make your morn- ing commute longer than usual, you can make a pretty good estimate about what time you need to leave home in order to get to work on time. If you have a super-important morning meeting, you might leave earlier to allow for delays. We all have an innate sense of how to deal with uncertain situa- tions and reason about uncertainty. When you think this way, you’re start- ing to think probabilistically.

Brief Contents
Contents in Detail
Acknowledgments
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
Index