I will give you two references of possible interest and an alternative method to your (non trivial) problem. References: Generalised linear mixed model analysis via sequential Monte Carlo sampling SMCTC : sequential Monte Carlo in C++ An alternative for sampling from your mode...
Generalised linear mixed model analysis via sequential Monte Carlo sampling
- Citation data:
Electronic Journal of Statistics, ISSN: 1935-7524, Vol: 2, Issue: 0, Page: 916-938
- Publication Year:
- arXiv Id:
- Repository URL:
- https://ro.uow.edu.au/infopapers/2522; http://arxiv.org/abs/0810.1163
- Mathematics; carlo; monte; sequential; via; analysis; model; mixed; linear; sampling; generalised; Physical Sciences and Mathematics; Statistics - Computation
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 chainMonte Carlo (MCMC). The SequentialMonte Carlo sampler (SMC) is a new and generalmethod for producing samples from posterior distributions. In thisarticle 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. © 2008, Institute of Mathematical Statistics. All rights reserved.