Probabilistic generative modeling can help us discover structured representations from unsupervised time series data. I'll survey some basic ideas from Bayesian modeling and inference for time series and give examples of how they can be composed and extended. In particular, I'll focus on building up Bayesian switching linear dynamical systems (SLDS) and associated sampling and structured mean field inference algorithms, motivated by applications to behavior modeling from last week.
If time permits, I'll also talk about our current work on integrating these structured Bayesian generative models with the right amount of "neural net goo" to combine their respective strengths. I might also show some magic tricks with autograd.