Abstract

In this paper, we address the complex problem of sequential Bayesian estimation and model selection/averaging. This problem does not usually admit any type of closed-form analytical solutions and, as a result, one has to resort to numerical methods. We propose here an original and powerful sequential simulation-based strategy to perform the necessary computations. This strategy is based on Monte Carlo particle methods and model selection/averaging using predictive distributions. It combines sequential importance sampling, Rao-Blackwellisation, a selection procedure and reversible jump MCMC moves. We demonstrate the eectiveness of the method by performing inference and learning on a hybrid model consisting of a dynamic linear model and a dynamic mixture of kernel basis functions.

Citations