Sequential Bayesian Estimation And Model Selection Applied To Neural Networks

In this paper, we address the complex problem of sequential Bayesian estimation and model selection. 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 sequential simulation-based strategy to perform the necessary computations. It combines sequential importance sampling, a selection procedure and reversible jump MCMC moves. We demonstrate the effectiveness of the method by...