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0899-7667
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1530-888X
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Neural Computation

November 1, 2001, Vol. 13, No. 11, Pages 2549-2572
(doi: 10.1162/089976601753196021)
© 2001 Massachusetts Institute of Technology
Manifold Stochastic Dynamics for Bayesian Learning
Article PDF (197.89 KB)
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.