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 8. Modeling dynamic systems
This chapter covers
- Creating a probabilistic model of a dynamic system
- Using different kinds of dynamic models including Markov chains, hidden Markov models, and dynamic Bayesian networks
- Using probabilistic models to create new kinds of dynamic models, such as models with time-varying structure
- Monitoring a dynamic system in an ongoing manner
Over the past few chapters, you’ve learned a good deal about using probabilistic programming to build probabilistic models. At this point, you have many techniques in your pocket, including modeling dependencies, functions, collections, and object-oriented modeling. This chapter builds on these techniques to model a particularly important kind of system: a dynamic ...
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