Bias-Variance Tradeoffs: Novel Applications

Dev Rajnarayan; David Wolpert

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arXiv · 0810.0879

Bias-Variance Tradeoffs: Novel Applications

Dev Rajnarayan·David Wolpert

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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.

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BibTeX RIS

Dev Rajnarayan, David Wolpert. 2008-10-06. Bias-Variance Tradeoffs: Novel Applications. https://arxiv.org/abs/0810.0879

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