Hamiltonian and Langevin Monte Carlo

Physics might be on to something

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

Note salad from a Betancourt seminar

Michael Betancourt’s heuristic explanation of Hamiltonian Monte Carlo: sets of high mass, no good - we need the “typical 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? “Geometric” 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.

Discontinuous likelihood

The solution is MOAR PHYSICS; we can construct hamiltonians which sample based on reflection/refraction dynamics in the augmented state space; see Afshar and Domke (2015); Nishimura, Dunson, and Lu (2020).


Manifold Monte Carlo.

Understanding NUTS and HMC | George Ho

In terms of reading code, I'd recommend looking through Colin Carroll's minimc for a minimal working example of NUTS in Python, written for pedagogy rather than actual sampling. For a "real world" implementation of NUTS/HMC, I'd recommend looking through my littlemcmc for a standalone version of PyMC3's NUTS/HMC samplers.


Afshar, Hadi Mohasel, and Justin Domke. 2015. “Reflection, Refraction, and Hamiltonian Monte Carlo,” 9.
Bales, Ben, Arya Pourzanjani, Aki Vehtari, and Linda Petzold. 2019. Selecting the Metric in Hamiltonian Monte Carlo.” arXiv:1905.11916 [Stat], May.
Betancourt, Michael. 2017. A Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv:1701.02434 [Stat], January.
———. 2018. The 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. The 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. The Stan Math Library: Reverse-Mode Automatic Differentiation in C++.” arXiv Preprint arXiv:1509.07164.
Caterini, Anthony L., Arnaud Doucet, and Dino Sejdinovic. 2018. Hamiltonian Variational Auto-Encoder.” In Advances in Neural Information Processing Systems.
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Devlin, Lee, Paul Horridge, Peter L Green, and Simon Maskell. 2021. “The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel,” 5.
Durmus, Alain, and Eric Moulines. 2016. High-Dimensional Bayesian Inference via the Unadjusted Langevin Algorithm.” arXiv:1605.01559 [Math, Stat], May.
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Hoffman, M D, and A Gelman. 2011. “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.” Arxiv Preprint arXiv:1111.4246.
Liu, Meng, Haoran Liu, and Shuiwang Ji. 2021. Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models,” November.
Ma, Yi-An, Tianqi Chen, and Emily B. Fox. 2015. A Complete Recipe for Stochastic Gradient MCMC.” In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2, 2917–25. NIPS’15. Cambridge, MA, USA: MIT Press.
Mangoubi, Oren, and Aaron Smith. 2017. Rapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions.” arXiv:1708.07114 [Math, Stat], August.
Margossian, Charles C., Aki Vehtari, Daniel Simpson, and Raj Agrawal. 2020. Hamiltonian Monte Carlo Using an Adjoint-Differentiated Laplace Approximation: Bayesian Inference for Latent Gaussian Models and Beyond.” arXiv:2004.12550 [Stat], October.
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Neal, Radford M. 2011. MCMC 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.
Nishimura, Akihiko, David B Dunson, and Jianfeng Lu. 2020. Discontinuous Hamiltonian Monte Carlo for Discrete Parameters and Discontinuous Likelihoods.” Biometrika 107 (2): 365–80.
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