Stochastic Gradient Monomial Gamma Sampler

Abstract

Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is often poor. This results in inadequate exploration of the posterior distribution. A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A generalized kinetic function is leveraged, delivering superior stationary mixing, especially for multimodal distributions. Techniques are also discussed to overcome the practical issues introduced by this generalization. It is shown that the proposed approach is better at exploring complex multimodal posterior distributions, as demonstrated on multiple applications and in comparison with other stochastic gradient MCMC methods.

Published on ICML 2017

References in corpus (4)

• Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
• A Complete Recipe for Stochastic Gradient MCMC
• On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
• Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

Cited by in corpus (2)

• Modified Hamiltonian Monte Carlo for Bayesian inference
• Understanding MCMC Dynamics as Flows on the Wasserstein Space