Accelerating sequential Monte Carlo with surrogate likelihoods

TL;DR

This paper introduces a novel adaptive surrogate likelihood approach within sequential Monte Carlo methods to efficiently handle Bayesian models with costly likelihood evaluations, significantly reducing computational effort.

Contribution

It develops an adaptive surrogate likelihood framework combined with annealing schedules and optimization strategies to improve efficiency in SMC for expensive likelihoods.

Findings

Reduces likelihood evaluations in Bayesian inference

Improves computational efficiency with surrogate models

Demonstrates effectiveness on real and toy examples

Abstract

Delayed-acceptance is a technique for reducing computational effort for Bayesian models with expensive likelihoods. Using a delayed-acceptance kernel for Markov chain Monte Carlo can reduce the number of expensive likelihoods evaluations required to approximate a posterior expectation. Delayed-acceptance uses a surrogate, or approximate, likelihood to avoid evaluation of the expensive likelihood when possible. Within the sequential Monte Carlo framework, we utilise the history of the sampler to adaptively tune the surrogate likelihood to yield better approximations of the expensive likelihood, and use a surrogate first annealing schedule to further increase computational efficiency. Moreover, we propose a framework for optimising computation time whilst avoiding particle degeneracy, which encapsulates existing strategies in the literature. Overall, we develop a novel algorithm for computationally efficient SMC with expensive likelihood functions. The method is applied to static Bayesian models, which we demonstrate on toy and real examples, code for which is available at https://github.com/bonStats/smcdar.