  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.
