Variance Reduction Techniques in Monte Carlo Methods

Jack P.C. Kleijnen, A.A.N. Ridder, R.Y. Rubinstein

Research output: Working paperDiscussion paperOther research output

349 Downloads (Pure)

Abstract

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develop ever more realistic models, so that the net result has not been faster execution of simulation experiments; e.g., some modern simulation models need hours or days for a single ’run’ (one replication of one scenario or combination of simulation input values). Moreover there are some simulation models that represent rare events which have extremely small probabilities of occurrence), so even modern computer would take ’for ever’ (centuries) to execute a single run - were it not that special VRT can reduce theses excessively long runtimes to practical magnitudes.
Original languageEnglish
Place of PublicationTilburg
PublisherInformation Management
Number of pages18
Volume2010-117
Publication statusPublished - 2010

Publication series

NameCentER Discussion Paper
Volume2010-117

Keywords

  • common random numbers
  • antithetic random numbers
  • importance sampling
  • control variates
  • conditioning
  • stratied sampling
  • splitting
  • quasi Monte Carlo

Fingerprint Dive into the research topics of 'Variance Reduction Techniques in Monte Carlo Methods'. Together they form a unique fingerprint.

  • Cite this

    Kleijnen, J. P. C., Ridder, A. A. N., & Rubinstein, R. Y. (2010). Variance Reduction Techniques in Monte Carlo Methods. (CentER Discussion Paper; Vol. 2010-117). Information Management.