Generalised linear mixed model analysis via sequential Monte Carlo sampling

TL;DR

This paper introduces a sequential Monte Carlo sampler for Bayesian inference in generalized linear mixed models, offering a competitive alternative to traditional MCMC methods, with demonstrated effectiveness on simulated and real datasets.

Contribution

The paper presents a novel SMC-based algorithm for GLMM inference, expanding Bayesian analysis tools beyond MCMC techniques.

Findings

SMC method performs well on simulated data

SMC is effective on real datasets

Competitive with existing MCMC methods

Abstract

We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chain Monte Carlo (MCMC). The Sequential Monte Carlo sampler (SMC) is a new and general method for producing samples from posterior distributions. In this article we demonstrate use of the SMC method for performing inference for GLMMs. We demonstrate the effectiveness of the method on both simulated and real data, and find that sequential Monte Carlo is a competitive alternative to the available MCMC techniques.