Chapter 1. Thinking Probabilistically - A Bayesian Inference Primer
Probability theory is nothing but common sense reduced to calculation. | ||
| --Pierre-Simon Laplace | ||
In this chapter, we will learn the core concepts of Bayesian statistics and some of the instruments in the Bayesian toolbox. We will use some Python code in this chapter, but this chapter will be mostly theoretical; most of the concepts in this chapter will be revisited many times through the rest of the book. This chapter, being intense on the theoretical side, may be 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
- Installing all Python packages
