Uploaded May 2018 | Updated September 2026, 56 minutes ago
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: 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: amazon.co.uk/Students-Guide-Bayesian-Statistics/dp/1473916364
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG&disable_polymer=true
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: 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: amazon.co.uk/Students-Guide-Bayesian-Statistics/dp/1473916364
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG&disable_polymer=true
