Sequential Monte Carlo for Generalized Linear Mixed Models
Seminar Room 1, Newton Institute
Sequential Monte Carlo methods for static problems combine the advantages of importance sampling and Markov chain based methods. We demonstrate how to use these exciting new techniques to fit generalised linear mixed models. A normal approximation to the likelihood is used to generate an initial sample, then transition kernels, reweighting and resampling result in evolution to a sample from the full posterior distribution. Since the technique does not rely on any ergodicity properties of the transition kernels, we can modify these kernels adaptively, resulting in a more efficient sampler.