Chapter 9. The three rules of probabilistic inference
This chapter covers
- Three important rules for working with probabilistic models:
- The chain rule, which lets you build complex models out of simple components
- The total probability rule, which lets you simplify a complex probabilistic model to answer simple queries
- Bayes’ rule, with which you can draw conclusions about causes from observations of their effects
- The basics of Bayesian modeling, including how to estimate model parameters from data and use them to predict future cases
In part 2 of this book, you learned all about writing probabilistic programs for a variety of applications. You know that probabilistic programming systems use inference algorithms operating on these programs ...