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Neural Computation, Vol 10, 749-770, Copyright © 1998 by The MIT Press

LETTERS

Issues in Bayesian Analysis of Neural Network Models

Peter Muller and David Rios Insua

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed-architecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data-driven procedure to identify sensible architectures.


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