Learning Markov Chain Monte Carlo through Progression： Monte Carlo, Importance Weights, MCMC, HMC

This video presents Markov Chain Monte Carlo (MCMC) by first reviewing standard Monte Carlo and building up concepts via Importance Sampling (Importance Weights) and Accept/Reject Sampling (using Importance Weights and Uniform random variables to remove samples) before using said concepts to truly understand the MCMC algorithms: Metropolis-Hastings and Hamiltonian Monte Carlo. A Git repository containing R code for each sampling algorithm shown in the video: https://github.com/goetzman-pdx/mcmc.git 0:00 Introduction 0:53 Monte Carlo 2:57 Importance Sampling 5:50 Accept/Reject Sampling 8:06 Metropolis-Hastings 12:01 Hamiltonian Monte Carlo