Optimal resource allocation in two stage sampling of input distributions

Achal Bassamboo*, Sandeep Juneja

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Consider a performance measure that is evaluated via Monte Carlo simulation where input distributions to the underlying model may involve two stage sampling. The settings of interest include the case where in the first stage physical samples from the distribution are collected. In the second stage, Monte Carlo sampling is done from the observed empirical distribution. We also consider the sampling-importance resampling (SIR) algorithm. Here it is difficult to sample directly from the desired input distribution, and these samples are generated in two stages. In the first stage, a large number of samples are generated from a distribution convenient from the sampling viewpoint. In the second stage, a resampling is done from the samples generated in the first stage so that asymptotically the new samples have the desired distribution. We discuss how to allocate computational and other effort optimally the two stages to minimize the estimator's resultant mean square error.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 Winter Simulation Conference, WSC
Number of pages6
StatePublished - Dec 1 2006
Event2006 Winter Simulation Conference, WSC - Monterey, CA, United States
Duration: Dec 3 2006Dec 6 2006

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2006 Winter Simulation Conference, WSC
CountryUnited States
CityMonterey, CA

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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