An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section)
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.
Cite
Text
Malzahn and Opper. "An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section)." Journal of Machine Learning Research, 2003.Markdown
[Malzahn and Opper. "An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section)." Journal of Machine Learning Research, 2003.](https://mlanthology.org/jmlr/2003/malzahn2003jmlr-approximate/)BibTeX
@article{malzahn2003jmlr-approximate,
title = {{An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section)}},
author = {Malzahn, Dörthe and Opper, Manfred},
journal = {Journal of Machine Learning Research},
year = {2003},
pages = {1151-1173},
volume = {4},
url = {https://mlanthology.org/jmlr/2003/malzahn2003jmlr-approximate/}
}