Towards unifying hamiltonian Monte Carlo and Slice sampling

Published

Conference Paper

© 2016 NIPS Foundation - All Rights Reserved. We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics. This insight enables extension of HMC and slice sampling to a broader family of samplers, called Monomial Gamma Samplers (MGS). We provide a theoretical analysis of the mixing performance of such samplers, proving that in the limit of a single parameter, the MGS draws decorrelated samples from the desired target distribution. We further show that as this parameter tends toward this limit, performance gains are achieved at a cost of increasing numerical difficulty and some practical convergence issues. Our theoretical results are validated with synthetic data and real-world applications.

Duke Authors

Cited Authors

  • Zhang, Y; Wang, X; Chen, C; Henao, R; Fan, K; Carin, L

Published Date

  • January 1, 2016

Published In

Start / End Page

  • 1749 - 1757

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus