Variational Hamiltonian Monte Carlo via Score Matching

TL;DR

This paper introduces a novel variational Hamiltonian Monte Carlo method that uses neural network surrogates to reduce gradient computations, enhancing efficiency in large-scale Bayesian inference.

Contribution

It combines variational approximation with Hamiltonian Monte Carlo using neural network surrogates to improve scalability and reduce computational costs.

Findings

Reduces gradient computation in HMC using neural network surrogates

Achieves faster exploration of parameter space

Demonstrates effectiveness on synthetic and real datasets

Abstract

Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC). In recent years, however, several methods have been proposed based on combining variational Bayesian inference and MCMC simulation in order to improve their overall accuracy and computational efficiency. This marriage of fast evaluation and flexible approximation provides a promising means of designing scalable Bayesian inference methods. In this paper, we explore the possibility of incorporating variational approximation into a state-of-the-art MCMC method, Hamiltonian Monte Carlo (HMC), to reduce the required gradient computation in the simulation of Hamiltonian flow, which is the bottleneck for many applications of HMC in big data problems. To this end, we use a {\it free-form} approximation induced by a fast and flexible surrogate function based on single-hidden layer feedforward neural networks. The surrogate provides sufficiently accurate approximation while allowing for fast exploration of parameter space, resulting in an efficient approximate inference algorithm. We demonstrate the advantages of our method on both synthetic and real data problems.