Anticorrelated discrete-time stochastic simulation

Peter A. Maginnis, Matthew West, Geir E. Dullerud

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

Abstract

We provide the first known rigorous theoretical analysis of previously published anticorrelated variance reduction techniques for tau-leaping systems. These algorithms provide a way to reduce the expected MSE of mean estimators by introducing local negative correlation between Monte Carlo sample paths. We prove a recursive equation governing the evolution of these covariances in both the nonlinear and linear cases. Further, we prove sufficient algebraic conditions for variance reduction in the linear rates case that require no stochastic simulation. Finally, we present an example system to illustrate both the application of these tests and to demonstrate their effectiveness.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages618-623
Number of pages6
ISBN (Print)9781467357173
DOIs
StatePublished - 2013
Externally publishedYes
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
Country/TerritoryItaly
CityFlorence
Period12/10/1312/13/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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