Bayesian model comparison via path-sampling sequential Monte Carlo

Model comparison for the purposes of selection, averaging and validation is a problem which is found throughout statistics and signal processing. Within the Bayesian paradigm, these problems all require the calculation of the posterior probabilities of models within a particular class. Substantial progress has been made in recent years, but there are numerous difficulties in the practical implementation of existing schemes. This paper develops sequential Monte Carlo (SMC) sampling strategies to characterize the posterior distribution of a collection of models, as well as the parameters of those models which go some way towards addressing the difficulties encountered using other techniques. In particular, efficient characterization of the quantities of interest is provided using only within model simulation. Proof-of-concept simulations demonstrate the robustness and potential performance of the algorithm, particular via parallel implementation using a GPU or multi-core processor.