Hamiltonian and Langevin Monte Carlo

Physics might be on to something

Hamiltonians, energy conservation in sampling. Handy. Summary would be nice.

Michael Betancourt’s heuristic explanation of Hamiltonian Monte Carlo: sets of high mass, no good - we need the β€œtypical set”, a set whose product of differential volume and density is high. Motivates Markov Chain Monte Carlo on this basis, a way of exploring typical set given points already in it, or getting closer to the typical set if starting without. How to get a central limit theorem? β€œGeometric” ergodicity results. Hamiltonian Monte Carlo is a procedure for generating measure-preserving floes over phase space

\[H(q,p)=-\log(\pi(p|q)\pi(q))\] So my probability density gradient influences the particle momentum. And we can use symplectic integrators to walk through trajectories (if I knew more numerical quadrature I might know more about the benefits of this) in between random momentum perturbations. Some more stuff about resampling trajectories to de-bias numerical error, which is the NUTS extension to HMC.

Langevin Monte Carlo


To file

Manifold Monte Carlo.


Betancourt, Michael. 2017. β€œA Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv:1701.02434 [Stat], January.
β€”β€”β€”. 2018. β€œThe Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo.” Annalen Der Physik, March.
Betancourt, Michael, Simon Byrne, Sam Livingstone, and Mark Girolami. 2017. β€œThe Geometric Foundations of Hamiltonian Monte Carlo.” Bernoulli 23 (4A): 2257–98.
Carpenter, Bob, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael Betancourt. 2015. β€œThe Stan Math Library: Reverse-Mode Automatic Differentiation in C++.” arXiv Preprint arXiv:1509.07164.
Durmus, Alain, and Eric Moulines. 2016. β€œHigh-Dimensional Bayesian Inference via the Unadjusted Langevin Algorithm.” arXiv:1605.01559 [Math, Stat], May.
Girolami, Mark, and Ben Calderhead. 2011. β€œRiemann Manifold Langevin and Hamiltonian Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 (2): 123–214.
Goodrich, Ben, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Bob Carpenter, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. β€œStan : A Probabilistic Programming Language.” Journal of Statistical Software 76 (1).
Neal, Radford M. 2011. β€œMCMC Using Hamiltonian Dynamics.” In Handbook for Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng. Boca Raton: Taylor & Francis.
Norton, Richard A., and Colin Fox. 2016. β€œTuning of MCMC with Langevin, Hamiltonian, and Other Stochastic Autoregressive Proposals.” arXiv:1610.00781 [Math, Stat], October.
Xifara, T., C. Sherlock, S. Livingstone, S. Byrne, and M. Girolami. 2014. β€œLangevin Diffusions and the Metropolis-Adjusted Langevin Algorithm.” Statistics & Probability Letters 91 (Supplement C): 14–19.

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