A Variational Bayesian Committee of Neural Networks
Neil David Lawrence, Mehdi Azzouzi · 1999
Exact inference in Bayesian neural networks is non analytic to compute, approximate methods 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