- Statist. Sci.
- Volume 19, Number 1 (2004), 128-139.
Bayesian Methods for Neural Networks and Related Models
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.
Statist. Sci., Volume 19, Number 1 (2004), 128-139.
First available in Project Euclid: 14 July 2004
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Titterington, D. M. Bayesian Methods for Neural Networks and Related Models. Statist. Sci. 19 (2004), no. 1, 128--139. doi:10.1214/088342304000000099. https://projecteuclid.org/euclid.ss/1089808278