期刊
STATISTICAL SCIENCE
卷 19, 期 1, 页码 128-139出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/088342304000000099
关键词
Bayesian methods; Bayesian model choice; feed-forward neural network; graphical model; Laplace approximation; machine learning; Markov chain Monte Carlo; variational approximation
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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