Bias-Variance Tradeoffs: Novel Applications

Details Bias-Variance Tradeoffs: Novel Applications Bias-Variance Tradeoffs: Novel Applications

2008-10-06

arxiv.org/abs/0810.0879v1

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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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