# Optimal learning for stochastic optimization with nonlinear parametric belief models

## Author(s): He, X; Powell, William B

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1456g
DC FieldValueLanguage
dc.contributor.authorHe, X-
dc.contributor.authorPowell, William B-
dc.date.accessioned2021-10-11T14:17:48Z-
dc.date.available2021-10-11T14:17:48Z-
dc.date.issued2018-01-01en_US
dc.identifier.citationHe, X, Powell, WB. (2018). Optimal learning for stochastic optimization with nonlinear parametric belief models. SIAM Journal on Optimization, 28 (3), 2327 - 2359. doi:10.1137/16M1073042en_US
dc.identifier.issn1052-6234-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1456g-
dc.description.abstract© 2018 Society for Industrial and Applied Mathematics. We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameters.Our goal is to maximize an objective function represented by a nonlinear parametric belief model,while simultaneously learning the unknown parameters, by guiding a sequential experimentationprocess which is expensive. We overcome the problem of computing the expected value of an experiment, which is computationally intractable, by using a sampled approximation, which helps toguide experiments but does not provide an accurate estimate of the unknown parameters. We thenintroduce a resampling process which allows the sampled model to adapt to new information, exploiting past experiments. We show theoretically that the method generates sequences that convergeasymptotically to the true parameters, while simultaneously maximizing the objective function. Weshow empirically that the process exhibits rapid convergence, yielding good results with a very smallnumber of experiments.en_US
dc.format.extent2327 - 2359en_US
dc.language.isoen_USen_US
dc.relation.ispartofSIAM Journal on Optimizationen_US
dc.rightsAuthor's manuscripten_US
dc.titleOptimal learning for stochastic optimization with nonlinear parametric belief modelsen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1137/16M1073042-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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