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  A practical Monte Carlo implementation of Bayesian learning

Rasmussen, C. (1996). A practical Monte Carlo implementation of Bayesian learning. Advances in Neural Processing Systems 8, 598-604.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-EB64-D Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-EB65-B
Genre: Conference Paper

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 Creators:
Rasmussen, CE1, Author           
Touretzky, Editor
D.S., Editor
Mozer, M.C., Editor
Hasselmo, M.E., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 datalimited tasks from real world domains.

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 Dates: 1996-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-20107-0
URI: http://books.nips.cc/nips08.html
BibTex Citekey: 2999
 Degree: -

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Title: Ninth Annual Conference on Neural Information Processing Systems (NIPS 1995)
Place of Event: Denver, CO, USA
Start-/End Date: -

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Title: Advances in Neural Processing Systems 8
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 598 - 604 Identifier: -