A Variational Mean-Field Theory for Sigmoidal Belief Networks

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Chiranjib Bhattacharyya, S. Keerthi


A variational derivation of Plefka's mean-field theory is presented. This theory is then applied to sigmoidal belief networks with the aid of further approximations. Empirical evaluation on small scale networks show that the proposed approximations are quite com(cid:173) petitive.