The intuition behind the Hamiltonian Monte Carlo algorithm

Explains the physical analogy that underpins the Hamiltonian Monte Carlo (HMC) algorithm. It then goes onto explain that HMC can be viewed as a specific type of Metropolis-Hastings sampler. The paper by Michael Betancourt I mention is "A Conceptual Introduction to Hamiltonian Monte Carlo", 2018, ArXiv, and is available here: https://arxiv.org/pdf/1701.02434.pdf. The Radford Neal paper is, "MCMC using Hamiltonian dynamics", Chapter 5 in the "Handbook of Markov Chain Monte Carlo" by Brooks et al., 2011. This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: https://www.amazon.co.uk/Students-Guide-Bayesian-Statistics/dp/1473916364 For more information on all things Bayesian, have a look at: https://ben-lambert.com/bayesian/. The playlist for the lecture course is here: https://www.youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG&disable_polymer=true