Decomposed slice sampling for factorized distributions

Abstract

Slice sampling provides an automatical adjustment to match the characteristics of the distribution. Although this method has made great success in many situations, it becomes limited when the distribution is complex. Inspired by Higdon [1], in this paper, we present a decomposed sampling framework based on slice sampling called decomposed slice sampling (DSS). We suppose that the target distribution can be divided into two multipliers so that information in each term can be used, respectively. The first multiplier is used in the first step of DSS to obtain horizontal slices and the last term is used in the second step. Simulations on four simple distributions indicate the effectiveness of our method. Compared with slice sampling and Hamiltonian Monte Carlo on Gaussian distributions in different dimensions and ten real-world datasets, the proposed method achieves better performance.