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A Variational Bayesian Committee of Neural Networks
, 1999.
Abstract
Exact inference in Bayesian neural networks is non analytic to compute and
as a result approximate approaches such as the evidence procedure, Monte-Carlo sampling
and variational inference have been proposed. In this paper we present a general
overview of the Bayesian approach with a particular emphasis on the variational
procedure. We then present a new approximating distribution based on *mixtures*
of Gaussian distributions and show how it may be implemented. We present results
on a simple toy problem and on two real world data-sets.