Python implementation of Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale¹

¹ (C Wu, J Stoehr, CP Robert - 2019)

We implemented and tested the Empirical Hamiltonian Monte Carlo (eHMC) and Partially Refreshed Hamiltonian Monte Carlo (prHMC) proposed in the paper mentioned above. These are HMC-based Markov Chain Monte Carlo (MCMC) methods that allow us to sample from complicated distributions (which we may know only up to a constant) efficiently.

We ran a simpler version of the Experiment 1 of the paper, using tensorflow_probability implementtion of NUTS as benchmark.