Variational methods for approximate reasoning in graphical models
Exact inference in large and complex graphical models e g Bayesian networks is computationally intractable Approximate schemes are therefore of great importance for real world computation In this paper we consider a general scheme in which the original intractable graphical model is approximated by a model with a tractable structure The approximatingmodel is optimised by an iterative procedure which minimises the Kullback Leibler divergence between the two models The procedure is guaranteed to converge to a local minimum of the Kullback Leibler divergence The scheme provides a bridge between naive mean eld theory and exact computation Simulation results are provided to illustrate the method