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 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 ...
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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.