Robust full Bayesian methods for neural networks

Conference item

In this paper, we propose a full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We then propose a reversible jump Markov chain Monte Carlo (MCMC) method to perform the necessary computations. We find that the results are not only better than the previously reported ones, but also appear to be robust with respect to the prior specification. Moreover, we present a geometric convergence theorem for the algorithm.

Authors: Andrieu, C; de Freitas, J; Doucet, A

• Publication status: Published
• Publisher: Neural information processing systems foundation
• Host title: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12
• Volume: 12
• Pages: 379-385
• Publication date: 2000-01-01
• ISSN: 1049-5258
• ISBN: 0262194503