Appendices
A A Quick Introduction to R

Bayesian Statistics the Fun Way

Preface
PART I: Introduction to Probability
Introduction
1 Bayesian Thinking and Everyday Reasoning
2 Measuring Uncertainty
3 Logic of Uncertainty
4 Creating a Binomial Probability Distribution
5 The Beta Distribution
PART II: Bayesian Probability and Prior Probabilities
6 Conditional Probability
7 Bayes’ Theorem With LEGO
8 The Prior, Likelihood, and Posterior of Bayes’ Theorem
9 Bayesian Priors and Working with Probability Distributions
PART III: Parameter Estimation
10 Introduction to Averaging and Parameter Estimation
11 Measuring the Spread of our Data
12 The Normal Distribution
13 Tools of Parameter Estimation: The PDF, CDF, and Quantile Function
14 Parameter Estimation with Prior Probabilities
PART IV: Hypothesis Testing: The Heart of Statistics
15 From Parameter Estimation to Hypothesis Testing: Building a Bayesian A/B Test
16 Introduction to the Bayes Factor and Posterior Odds: The Competition of Ideas
17 Bayesian Reasoning in the Twilight Zone
18 When Data Doesn’t Convince
19 From Hypothesis Testing to Parameter Estimation
References
Appendices
- A A Quick Introduction to R
- B Enough Calculus to Get By

Appendices
A A Quick Introduction to R

Appendix A: A Quick Introduction to R

This section is empty, because there is nothing new for me to try or discuss.

B Enough Calculus to Get By

Source Code

# A Quick Introduction to R

This section is empty, because there is nothing new for me to try or discuss.