Statistical Science

Bayesian Methods for Neural Networks and Related Models

D. M. Titterington

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Abstract

Models such as feed-forward neural networks and certain other structures investigated in the computer science literature are not amenable to closed-form Bayesian analysis. The paper reviews the various approaches taken to overcome this difficulty, involving the use of Gaussian approximations, Markov chain Monte Carlo simulation routines and a class of non-Gaussian but “deterministic” approximations called variational approximations.

Article information

Source
Statist. Sci. Volume 19, Number 1 (2004), 128-139.

Dates
First available: 14 July 2004

Permanent link to this document
http://projecteuclid.org/euclid.ss/1089808278

Digital Object Identifier
doi:10.1214/088342304000000099

Mathematical Reviews number (MathSciNet)
MR2082152

Zentralblatt MATH identifier
1057.62078

Citation

Titterington, D. M. Bayesian Methods for Neural Networks and Related Models. Statistical Science 19 (2004), no. 1, 128--139. doi:10.1214/088342304000000099. http://projecteuclid.org/euclid.ss/1089808278.


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