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Practical Probabilistic Programming by Avi Pfeffer

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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 ...

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