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Sammenlign metoder

Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.

Teknikker for variansreduksjon for Monte Carlo-simulering×Monte Carlo-simulering×
FagfeltSimuleringBeslutningstaking
FamilieProcess / pipelineMCDM
Opprinnelsesår1950s–1980s (technique family)1949
OpphavspersonHammersley & Morton (antithetic variates, 1956); Lavenberg & Welch (control variates, 1981); importance sampling roots in Kahn & Marshall (1953)Metropolis, N., Ulam, S.
TypeSimulation variance-reduction technique familyRobustness wrapper — Monte Carlo uncertainty propagation
Opprinnelig kildeRoss, S.M. (2012). Simulation (5th ed.). Academic Press. ISBN: 978-0124158252Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Aliasantithetic variates, control variates, importance sampling, stratified sampling MC
Relaterte40
SammendragVariance 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.MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateSammenlign metoder: Variance Reduction for Monte Carlo · MONTE-CARLO-SIMULATION. Hentet 2026-06-17 fra https://scholargate.app/no/compare