Journal
STATISTICAL SCIENCE
Volume 19, Issue 1, Pages 128-139Publisher
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/088342304000000099
Keywords
Bayesian methods; Bayesian model choice; feed-forward neural network; graphical model; Laplace approximation; machine learning; Markov chain Monte Carlo; variational approximation
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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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