Delayed Acceptance Slice Sampling

TL;DR

This paper introduces delayed acceptance hybrid slice sampling methods that leverage cheap deterministic approximations to improve computational efficiency in sampling from complex distributions, especially in Bayesian inference.

Contribution

It proposes a novel delayed acceptance approach for hybrid slice samplers, demonstrating ergodicity and efficiency gains over traditional methods.

Findings

Improved computational efficiency in numerical experiments

Delayed acceptance slice sampling outperforms Metropolis-Hastings

Ergodicity of the proposed methods is established

Abstract

Slice sampling is a well-established Markov chain Monte Carlo method for (approximate) sampling of target distributions which are only known up to a normalizing constant. The method is based on choosing a new state on a slice, i.e., a superlevel set of the given unnormalized target density (with respect to a reference measure). However, slice sampling algorithms usually require per step multiple evaluations of the target density, and thus can become computationally expensive. This is particularly the case for Bayesian inference with costly likelihoods. In this paper, we exploit deterministic approximations of the target density, which are relatively cheap to evaluate, and propose delayed acceptance versions of hybrid slice samplers. We show ergodicity of the resulting slice sampling methods, discuss the superiority of delayed acceptance (ideal) slice sampling over delayed acceptance Metropolis-Hastings algorithms, and illustrate the benefits of our novel approach in terms improved computational efficiency in several numerical experiments.