2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
NeurIPS 2022accept
We demonstrate for the first time that ill-conditioned, non-smooth, constrained distributions in very high dimension, upwards of 100,000, can be sampled efficiently \emph{in practice}. Our algorithm incorporates constraints into the Riemannian version of Hamiltonian Monte Carlo and maintains sparsit…