Manifold Stochastic Dynamics for Bayesian Learning

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Mark Zlochin, Yoram Baram


We propose a new Markov Chain Monte Carlo algorithm which is a gen(cid:173) eralization of the stochastic dynamics method. The algorithm performs exploration of the state space using its intrinsic geometric structure, facil(cid:173) itating efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to perform at least as well as the best state-of-the-art method while consuming considerably less time.