Chapter 1
Thinking Probabilistically
Probability theory is nothing but common sense reduced to calculation. – Pierre Simon Laplace
In this chapter, we will learn about the core concepts of Bayesian statistics and some of the instruments in the Bayesian toolbox. We will use some Python code, but this chapter will be mostly theoretical; most of the concepts we will see here will be revisited many times throughout this book. This chapter, being heavy on the theoretical side, is perhaps a little anxiogenic for the coder in you, but I think it will ease the path to effectively applying Bayesian statistics to your problems.
In this chapter, we will cover the following topics:
Statistical modeling
Probabilities and uncertainty
Bayes’ theorem and statistical inference
Single-parameter inference and the classic coin-flip problem
Choosing priors and why people often don’t like them but should
Communicating a Bayesian analysis
