Bias-Variance Tradeoffs: Novel Applications
Applications
2008-10-07 v1
Abstract
We present several applications of the bias-variance decomposition, beginning with straightforward Monte Carlo estimation of integrals, but progressing to the more complex problem of Monte Carlo Optimization (MCO), which involves finding a set of parameters that optimize a parameterized integral. We present the similarity of this application to that of Parametric Learning (PL). Algorithms in this field use a particular interpretation of the bias-variance trade to improve performance. This interpretation also applies to MCO, and should therefore improve performance. We verify that this is indeed the case for a particular MCO problem related to adaptive importance sampling.
Cite
@article{arxiv.0810.0879,
title = {Bias-Variance Tradeoffs: Novel Applications},
author = {Dev Rajnarayan and David Wolpert},
journal= {arXiv preprint arXiv:0810.0879},
year = {2008}
}