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Stokastiske Differentialligninger (SDE'er)×Monte Carlo-simulering×
FagområdeSimuleringBeslutningstagning
FamilieProcess / pipelineMCDM
Oprindelsesår1944 (theory); 1992 (numerical framework)1949
OphavspersonKiyosi Itô (Itô calculus, 1944); Peter Kloeden & Eckhard Platen (numerical methods, 1992)Metropolis, N., Ulam, S.
TypeContinuous-time stochastic process modelRobustness wrapper — Monte Carlo uncertainty propagation
Oprindelig kildeØksendal, B. (2003). Stochastic Differential Equations: An Introduction with Applications (6th ed.). Springer. DOI ↗Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
AliasserSDE, Itô equations, Stokastik Diferansiyel Denklemler (SDE)
Relaterede40
ResuméStochastic differential equations (SDEs) are differential equation models that combine a deterministic drift term — governing the average tendency of a system — with a stochastic diffusion term driven by a Wiener process (Brownian motion). Pioneered through Itô calculus by Kiyosi Itô in 1944 and given a comprehensive numerical treatment by Kloeden and Platen in 1992, SDEs are the standard modelling language for continuous-time systems subject to random noise, including financial asset prices, population dynamics, and physical processes.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: Stochastic Differential Equations · MONTE-CARLO-SIMULATION. Hentet 2026-06-18 fra https://scholargate.app/da/compare