Translator Disclaimer
February 2004 Bayesian Methods for Neural Networks and Related Models
D. M. Titterington
Statist. Sci. 19(1): 128-139 (February 2004). DOI: 10.1214/088342304000000099

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.

Citation

Download Citation

D. M. Titterington. "Bayesian Methods for Neural Networks and Related Models." Statist. Sci. 19 (1) 128 - 139, February 2004. https://doi.org/10.1214/088342304000000099

Information

Published: February 2004
First available in Project Euclid: 14 July 2004

zbMATH: 1057.62078
MathSciNet: MR2082152
Digital Object Identifier: 10.1214/088342304000000099

Rights: Copyright © 2004 Institute of Mathematical Statistics

JOURNAL ARTICLE
12 PAGES


SHARE
Vol.19 • No. 1 • February 2004
Back to Top