Status: draft

Last modified: 2016-10-26

# Bayesian Analysis

This chapter is optional and not necessary to understand the rest. It will help readers who are less familiar with statistical inference, especially Bayesian inference, to fill in some gaps and to avoid later confusion. While other tools can do this (e.g. BUGS/STAN, PyMC, etc.) and they might be better for some applications, it might be illuminating to see how these tasks can be done using probabilistic programming languages.

Here are the sections in this chapter:

- Bayesian Conditioning – shows how you can do Bayesian conditioning and inference with probabilistic programs.
- Empirical Bayesian Analysis – shows how you can do Empirical Bayesian data analysis, an alternative to frequentist inference, e.g. Null Hypothesis Significance Testing.