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dc.contributor.authorSzechtman, Roberto
dc.date2006
dc.date.accessioned2014-01-22T20:25:52Z
dc.date.available2014-01-22T20:25:52Z
dc.date.issued2006
dc.identifier.citation2006. A Hilbert Space Approach to Variance Reduction. Elsevier Handbooks in Operations Research and Management Science: Simulation (edited by S.G. Henderson and B.L. Nelson), Elsevier, Amsterdam, pp 259-289.
dc.identifier.urihttp://hdl.handle.net/10945/38430
dc.descriptionElsevier Handbooks in Operations Research and Management Science: Simulation, pp 259-289.en_US
dc.description.abstractIn this chapter we explain variance reduction techniques from the Hilbert space standpoint, in the terminating simulation context. We use projection ideas to explain how variance is reduced, and to link different variance reduction techniques. Our focus is on the methods of control variates, conditional Monte Carlo, weighted Monte Carlo, stratification, and Latin hypercube sampling.en_US
dc.rightsdefined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleA Hilbert Space Approach to Variance Reductionen_US
dc.typeBook Chapteren_US
dc.contributor.departmentOperations Research (OR)


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