The Bias-Variance dilemma of the Monte Carlo method (2001)
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by
Mark Zlochin
,
Yoram Baram
| Venue: | in Artificial Neural Networks: ICANN |
| Citations: | 4 - 0 self |
BibTeX
@INPROCEEDINGS{Zlochin01thebias-variance,
author = {Mark Zlochin and Yoram Baram},
title = {The Bias-Variance dilemma of the Monte Carlo method},
booktitle = {in Artificial Neural Networks: ICANN},
year = {2001},
publisher = {Springer Verlag}
}
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Abstract
We investigate the setting in which Monte Carlo methods are used and draw a parallel to the formal setting of statistical inference. In particular, we find that Monte Carlo approximation gives rise to a bias-variance dilemma. We show that it is possible to construct a biased approximation schemes with a lower approximation error than a related unbiased algorithms.
Citations
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| 6 | Regularized greedy importance sampling - Southey, Schuurmans, et al. - 2002 |
| 1 | Smoothed Adaptive Importance Sampling - Zlochin, Baram - 2000 |
