Practical Probabilistic Programming

Chapter 5. Modeling dependencies with Bayesian and Markov networks

This chapter covers

• Types of relationships among variables in a probabilistic model and how these relationships translate into dependencies
• How to express these various types of dependencies in Figaro
• Bayesian networks: models that encode directed dependencies among variables
• Markov networks: models that encode undirected dependencies among variables
• Practical examples of Bayesian and Markov networks

In chapter 4, you learned about the relationships between probabilistic models and probabilistic programs, and you also saw the ingredients of a probabilistic model, which are variables, dependencies, functional forms, and numerical parameters. This chapter focuses on two ...