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On choosing the mass matrix for Hamiltonian Monte Carlo

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In this talk will first go through the basics of Hamiltonian Monte Carlo (HMC), and then discuss some old and some recent developments in the field, with a particular focus on the role of the covariance matrix of the momentum distribution.

Potential reading: Neal, R. M. (2012). Mcmc Using Hamiltonian Dynamics. arXiv:1206.1901. Girolami, M., & Calderhead, B. (2011). Riemann manifold langevin and hamiltonian monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2), 123–214. Betancourt, M. J., & Girolami, M. (2013). Hamiltonian monte carlo for hierarchical models. arXiv:1312.0906. Langmore, I., Dikovsky, M., Geraedts, S., Norgaard, P., & Behren, R. V. (2019). A condition number for hamiltonian monte carlo. arXiv:1905.09813.

This talk is part of the Machine Learning Reading Group @ CUED series.

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