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An approximate analytical approach to resampling averages

An approximate analytical approach to resampling averages
An approximate analytical approach to resampling averages
Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica "trick" of Statistical Physics and the TAP approach for approximate Bayesian inference. We demonstrate our approach on regression with Gaussian processes. A comparison with averages obtained by Monte-Carlo sampling shows that our method achieves good accuracy.
1151-1173
Malzahn, Doerthe
a91a02d2-026e-49cc-b5c2-09fac404aeb2
Opper, Manfred
f7f8690a-fdcb-46f0-857d-c4140648039b
Malzahn, Doerthe
a91a02d2-026e-49cc-b5c2-09fac404aeb2
Opper, Manfred
f7f8690a-fdcb-46f0-857d-c4140648039b

Malzahn, Doerthe and Opper, Manfred (2003) An approximate analytical approach to resampling averages. Journal of Machine Learning Research, 4, 1151-1173.

Record type: Article

Abstract

Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica "trick" of Statistical Physics and the TAP approach for approximate Bayesian inference. We demonstrate our approach on regression with Gaussian processes. A comparison with averages obtained by Monte-Carlo sampling shows that our method achieves good accuracy.

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Published date: December 2003
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259183
URI: http://eprints.soton.ac.uk/id/eprint/259183
PURE UUID: 48e509e1-c143-4e22-a807-323b8d5d7e88

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Date deposited: 15 Mar 2004
Last modified: 14 Mar 2024 06:20

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Contributors

Author: Doerthe Malzahn
Author: Manfred Opper

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