Mattias Villani: Optimal Tuning of Subsampling Hamiltonian Monte Carlo,

Abstract: Hamiltonian Monte Carlo (HMC) is an increasingly popular simulation algorithm for Bayesian inference which has proven to be especially suitable in high-dimensional problems. A drawback of HMC is that it requires a large number of evaluations of the posterior gradient, which can be computationally costly, particularly in problems with large datasets. I will present our work on accelerating HMC by data subsampling and our recent results on how to optimally tune the algorithm.