Manifold Stochastic Dynamics for Bayesian Learning

Authors:
Mark Zlochin;Yoram Baram
Affiliations:
Department of Computer Science, Technion — Israel Institute of Technology, Technion City, Haifa 32000, Israel;Department of Computer Science, Technion — Israel Institute of Technology, Technion City, Haifa 32000, Israel
Venue:
Neural Computation
Year:
2001

Citing 7
Cited 1

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Abstract

We propose a new Markov Chain Monte Carlo algorithm, which is a generalization of the stochastic dynamics method. The algorithm performs exploration of the state-space using its intrinsic geometric structure, which facilitates efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to produce results comparable to the best state-of-the-art method while consuming considerably less time.