Bias-Variance Tradeoffs: Novel Applications

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dc.creatorRajnarayan, Dev
dc.creatorWolpert, David
dc.date2008-10-06
dc.date.accessioned2026-07-07T10:07:46Z
dc.date.available2026-07-07T10:07:46Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/0810.0879
dc.identifierhttp://arxiv.org/abs/0810.0879
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170761
dc.subjectApplications
dc.titleBias-Variance Tradeoffs: Novel Applications
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