Sequential Bayesian Estimation And Model Selection Applied To Neural Networks
Andrieu, C., Jordan Freitas, Arnaud Doucet, Cambridge Univ. (United Kingdom). Dept. of Engineering · 1999
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 applying it to radial basis function networks.