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Variance Reduction for Monte Carlo

Variance reduction techniques are a family of methods that improve the efficiency of Monte Carlo simulation by achieving the same estimation accuracy with fewer random draws. Developed incrementally from the 1950s onward — with antithetic variates attributed to Hammersley and Morton, control variates formalised by Lavenberg and Welch, and importance sampling rooted in Kahn and Marshall — the family includes antithetic variates (AV), control variates (CV), importance sampling (IS), and stratification, each exploiting a different structural property of the target quantity to lower estimator variance without introducing bias.

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Variance Reduction Techniques for Monte Carlo Simulation (AV, CV, IS)

Taxonomisk metodpost · process-pipeline / simulation

Ross, S.M. (2012). Simulation (5th ed.). Academic Press. · ISBN 978-0124158252
Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer. · DOI 10.1007/978-0-387-21617-1

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