Practical Probabilistic Programming

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 ...