title: Issues in Bayesian Analysis of Neural Network Models
abstract: 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&hyphenarchitecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data&hyphendriven procedure to identify sensible architectures.
authors.affiliation: Duke University, Institute of Statistics and Decision Sciences, Durham NC, US, Box 90251, 27708. pm@isds.duke.edu
authors.initials: P
authors.lastname: Müller
authors.initials: DR
authors.lastname: Insua