Babak Shahbaba

Precomputing strategy for Hamiltonian Monte Carlo Method based on regularity in parameter space

In this talk, we propose a strategy that exploits smoothness (regularity) of parameter space to improve computational efficiency of MCMC algorithms. When evaluation of functions are needed at a point in parameter space, interpolation from precomputed values or previous computed values is used. More specifically, we focus on Hamiltonian Monte Carlo (HMC) algorithms that use geometric information for faster exploration of probability distributions. Our proposed method is based on precomputing the required geometric information on a set of grids before running HMC. Sparse grid interpolation method is used for high dimensional problems.