March 2016
Intermediate to advanced
456 pages
16h 42m
English
Content preview from Practical Probabilistic ProgrammingBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.