Cited by 162Markov Chains and Monte Carlo MethodsGaussian Processes and Bayesian InferenceNeural Networks and Applications

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Bayesian Methods for Neural Networks and Related Models

Titterington, D. M.·2004·Statistical Science

DOI 10.1214/088342304000000099

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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.

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See all 162

Authors: Titterington, D. M.
Corresponding author: Titterington, D. M.
Venue: Statistical Science
Publisher: Institute of Mathematical Statistics
Published: 2004-02-01
Volume: 19
Issue: 1
Pages: 128-139
ISSN: 0883-4237 (ISSN-L)
2168-8745
DOI: 10.1214/088342304000000099
OpenAlex: W2019449965
Cited by: 162
References: 103
Citations by year: 2026: 2 · 2025: 9 · 2024: 20 · 2023: 16 · 2022: 17 · 2021: 9 · 2020: 9 · 2019: 7 · 2018: 6 · 2017: 7 · 2016: 10 · 2015: 9 · 2014: 8 · 2013: 3 · 2012: 1
FWCI: 3.05
Type: journal article
Language: en
Open access: closed
Locations: Statistical Science (publishedVersion)
Project Euclid (Cornell University) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: Bayesian neural networks, feedforward neural networks, Gaussian approximation, Markov chain Monte Carlo, variational approximation, computational intractability

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Bayesian Methods for Neural Networks and Related Models

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