Introduction** Part 1: Introduction to Probability **

Chapter 1 What Do You Believe and How Do You Change it?

Chapter 2 Measuring Uncertainty

Chapter 3 The Logic of Uncertainty

Chapter 4 Probability Distributions 1

Chapter 5 Probability Distributions 2** Part 2: Bayesian Probability and Prior Probabilities **

Chapter 6 Conditional Probability

Chapter 7 Bayes Theorem with LEGO

Chapter 8 Posterior, Likelihood, and Prior

Chapter 9 Working with Prior Probability Distributions** Part 3: Parameter Estimation**

Chapter 10 Intro to Parameter Estimation

Chapter 11 Measuring the Spread of Data

Chapter 12 Normal Distribution and Confidence

Chapter 13 Tools of Parameter Estimation

Chapter 14 Parameter Estimation with Priors** Part 4: Hypothesis Testing: The Heart of Statistics **

Chapter 15 From Parameter Estimation to Hypothesis Testing

Chapter 16 Comparing Hypotheses with Bayes Factor

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 Crash Course in R

Appendix B: Enough Calculus to Get By

Appendix C: Answers to Exercises

# Bayesian Statistics the Fun Way

**Get the most from your data, and have fun doing it**

Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don’t even understand, meaning they aren’t getting the most from it. *Bayesian Statistics the Fun Way* will change that.

This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid belt, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.

By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you’ll learn real skills, like how to:

- How to measure your own level of uncertainty in a conclusion or belief
- Calculate Bayes theorem and understand what it’s useful for
- Find the posterior, likelihood, and prior to check the accuracy of your conclusions
- Calculate distributions to see the range of your data
- Compare hypotheses and draw reliable conclusions from them

Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to *Bayesian Statistics the Fun Way* to get the most value from your data.