Enhancing Markov Chain Monte Carlo Sampling Methods with Deep Learning
Explore advanced techniques for enhancing Markov Chain Monte Carlo (MCMC) sampling methods using deep learning in this 46-minute lecture by Eric Vanden-Eijnden. Delve into key concepts including probabilities, expectations, and Monte-Carlo sampling before examining the intersection of sampling and learning. Investigate importance sampling, transport, and the application of normalizing flows to assist MCMC sampling. Learn about MCMC with normalizing flows for sampling random fields and Bayesian inference. Discover the Non-Equilibrium Importance Sampling (NEIS) technique and its applications to Gaussian mixtures and Neal's Funnel Distribution. Gain valuable insights into cutting-edge approaches for improving sampling methods in high-dimensional spaces.