Recorded Lectures: MCMC

The lectures, after an introduction to the problem, discuss two fundamental sampling techniques, rejection and importance sampling, and then turn their focus to MCMC, introducing the required background, the Metropolis Hastings algorithms, Gibbs sampling, and finally discussing some diagnostics and an extension of MCMC, Hamiltonian Monte Carlo, heavily used nowadays in inference engines, e.g. Pyro. 

1. Introduction
   
2. Rejection sampling
   
3. Importance sampling
   
4. Markov Chains
   
5. Detailed balance
   
6. Metropolis Hastings MCMC
   
7. Gibbs sampling
   
8. Convergence Diagnostics
   
9. Hamiltonian Monte Carlo