Mean Field Approximation in Bayesian Variable Selection

Details Mean Field Approximation in Bayesian Variable Selection Mean Field Approximation in Bayesian Variable Selection

1998-08-07

arxiv.org/abs/cond-mat/9808071v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

4 pages, 2 figures(5 ps files), uses epsf.sty, iconip98.sty, to appear in the proceedings of ICONIP'98-Kitakyushu

Scientific paper

Variable 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.

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