Practical Probabilistic Programming

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 ...