A Variational Bayesian Committee of Neural Networks
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.