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, may be a little anxiogenic for the coder in you, but I think it will ease the path in 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