# Hamiltonian and Langevin Monte Carlo

### Physics might be on to something

Usefulness: đź”§
Novelty: đź’ˇ
Uncertainty: đź¤Ş đź¤Ş đź¤Ş
Incompleteness: đźš§ đźš§ đźš§

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

Michael Betancourtâ€™s heuristic explanation of Hamiltonian Monte Carlo: sets of high mass, no good - we need the â€śtypical setâ€ť, 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? â€śGeometricâ€ť 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.

đźš§

## To file

Manifold Monte Carlo.

# Refs

Betancourt, Michael. 2017. â€śA Conceptual Introduction to Hamiltonian Monte Carlo,â€ť January. http://arxiv.org/abs/1701.02434.

â€”â€”â€”. 2018. â€śThe Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo.â€ť Annalen Der Physik, March. https://doi.org/10.1002/andp.201700214.

Betancourt, Michael, Simon Byrne, Sam Livingstone, and Mark Girolami. 2017. â€śThe Geometric Foundations of Hamiltonian Monte Carlo.â€ť Bernoulli 23 (4A): 2257â€“98. https://doi.org/10.3150/16-BEJ810.

Carpenter, Bob, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael Betancourt. 2015. â€śThe Stan Math Library: Reverse-Mode Automatic Differentiation in C++.â€ť arXiv Preprint arXiv:1509.07164. http://arxiv.org/abs/1509.07164.

Durmus, Alain, and Eric Moulines. 2016. â€śHigh-Dimensional Bayesian Inference via the Unadjusted Langevin Algorithm,â€ť May. http://arxiv.org/abs/1605.01559.

Girolami, Mark, and Ben Calderhead. 2011. â€śRiemann Manifold Langevin and Hamiltonian Monte Carlo Methods.â€ť Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 (2): 123â€“214. https://doi.org/10.1111/j.1467-9868.2010.00765.x.

Goodrich, Ben, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Bob Carpenter, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. â€śStan : A Probabilistic Programming Language.â€ť Journal of Statistical Software 76 (1). https://doi.org/10.18637/jss.v076.i01.

Neal, Radford M. 2011. â€śMCMC Using Hamiltonian Dynamics.â€ť In Handbook for Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng. Boca Raton: Taylor & Francis. http://arxiv.org/abs/1206.1901.

Norton, Richard A., and Colin Fox. 2016. â€śTuning of MCMC with Langevin, Hamiltonian, and Other Stochastic Autoregressive Proposals,â€ť October. http://arxiv.org/abs/1610.00781.

Xifara, T., C. Sherlock, S. Livingstone, S. Byrne, and M. Girolami. 2014. â€śLangevin Diffusions and the Metropolis-Adjusted Langevin Algorithm.â€ť Statistics & Probability Letters 91 (Supplement C): 14â€“19. https://doi.org/10.1016/j.spl.2014.04.002.