A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables

TL;DR

This paper introduces a hybrid scan Gibbs sampler combining systematic and random updates, which improves theoretical analysis and performance in Bayesian models with latent variables, demonstrated through various hierarchical models.

Contribution

The paper proposes a novel hybrid scan Gibbs sampler that blends systematic and random updates, facilitating easier analysis and enhanced performance over standard methods.

Findings

Hybrid scan Gibbs sampler is easier to analyze theoretically.

Adding sandwich steps improves the algorithm's performance.

Demonstrated effectiveness on multiple Bayesian hierarchical models.

Abstract

Gibbs sampling is a widely popular Markov chain Monte Carlo algorithm that can be used to analyze intractable posterior distributions associated with Bayesian hierarchical models. There are two standard versions of the Gibbs sampler: The systematic scan (SS) version, where all variables are updated at each iteration, and the random scan (RS) version, where a single, randomly selected variable is updated at each iteration. The literature comparing the theoretical properties of SS and RS Gibbs samplers is reviewed, and an alternative hybrid scan Gibbs sampler is introduced, which is particularly well suited to Bayesian models with latent variables. The word "hybrid" reflects the fact that the scan used within this algorithm has both systematic and random elements. Indeed, at each iteration, one updates the entire set of latent variables, along with a randomly chosen block of the remaining variables. The hybrid scan (HS) Gibbs sampler has important advantages over the two standard scan Gibbs samplers. Firstly, the HS algorithm is often easier to analyze from a theoretical standpoint. In particular, it can be much easier to establish the geometric ergodicity of a HS Gibbs Markov chain than to do the same for the corresponding SS and RS versions. Secondly, the sandwich methodology developed in Hobert and Marchev (2008), which is also reviewed, can be applied to the HS Gibbs algorithm (but not to the standard scan Gibbs samplers). It is shown that, under weak regularity conditions, adding sandwich steps to the HS Gibbs sampler always results in a theoretically superior algorithm. Three specific Bayesian hierarchical models of varying complexity are used to illustrate the results. One is a simple location-scale model for data from the Student's $t$ distribution, which is used as a pedagogical tool. The other two are sophisticated, yet practical Bayesian regression models.