An explanation of what MCMC (Markov-Chain-Monte Carlo) is and why you should care. I'll also explain things like what NUTS (No-U-Turn-Sampler) is and this will inform our future work on how to diagnose model performance.
Why should you care?
The underlying machinery of Probabilistic Programming is either a variational inference approach (largely out of scope for this course, but we'll talk about it) or a MCMC sampling approach. Like in any applied statistics topic you do need to have an understanding of how the underlying machinery works. In particular to debug it.
This section of the course provides an applied and practical focus on what MCMC samplers are. There'll be some Math but it'll be hacker focused.
- Metropolis sampling
- Gradient-based sampling methods