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 per-
forming inference is often extremely difficult, even when using the Bayesian
approach combined with Markov chainMonte Carlo (MCMC). The Sequen-
tialMonte Carlo sampler (SMC) is a new and generalmethod 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.