Issues in Bayesian Analysis of Neural Network Models

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 predict...

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Bibliographic Details
Published inNeural computation Vol. 10; no. 3; pp. 749 - 770
Main Authors Müller, Peter, Insua, David Rios
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.04.1998
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Simulation
Software
Markov chain
Monte Carlo method
Numerical analysis
Computer simulation
Prediction
Network architecture
Data transmission
Neural network
Mathematical model
Bayes decision
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Summary: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.
Bibliography:April, 1998
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ISSN:0899-7667
1530-888X
DOI:10.1162/089976698300017737