Mean Field Approximation in Bayesian Variable Selection

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dc.creatorIba, Yukito
dc.date1998-08-07
dc.date.accessioned2026-07-07T03:11:15Z
dc.date.available2026-07-07T03:11:15Z
dc.descriptionVariable selection for a multiple regression model (Noisy Linear Perceptron) is studied with a mean field approximation. In our Bayesian framework, variable selection is formulated as estimation of discrete parameters that indicate a subset of the explanatory variables. Then, a mean field approximation is introduced for the calculation of the posterior averages over the discrete parameters. An application to a real world example, Boston housing data, is shown.
dc.description4 pages, 2 figures(5 ps files), uses epsf.sty, iconip98.sty, to appear in the proceedings of ICONIP'98-Kitakyushu
dc.identifierhttps://arxiv.org/abs/cond-mat/9808071
dc.identifierhttp://arxiv.org/abs/cond-mat/9808071
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/28504
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.titleMean Field Approximation in Bayesian Variable Selection
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