References

Gradient Based MCMC

Hamiltonian Monte Carlo

An introduction

A Complete Recipe for Stochastic Gradient MCMC

Microcanonical Hamiltonian Monte Carlo

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

Microcanonical Hamiltonian Monte Carlo

Microcanonical Langevin Monte Carlo

Isokinetic Molecular Dynamics

Algorithms and novel applications based on the isokinetic ensemble. I. Biophysical and path integral molecular dynamics

Molecular Dynamics With Deterministic and Stochastic Numerical Methods

Sequential Monte Carlo Samplers

Overview (Probabilistic Machine Learning: Advanced Topics)

A detailed introduction

Numerical Integrators

Testing and tuning symplectic integrators for Hybrid Monte Carlo algorithm in lattice QCD

Differential Geometry

Purely mathematical introductions tend to be too rigorous for the present purposes, so notes on general relativity (which has differential geometry as its mathematical backbone) are recommended.

David Tong's lecture notes

General Relativity (Jetzer, Hähl), part III¹.

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1. The notation of Microcanonical Langevin Monte Carlo aligns with this book, so it is a useful reference point for the derivations. ↩