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نموذج ماركوف×محاكاة مونت كارلو×
المجالالمحاكاةاتخاذ القرار
العائلةProcess / pipelineMCDM
سنة النشأة19061949
صاحب الطريقةAndrei MarkovMetropolis, N., Ulam, S.
النوعProbabilistic state-transition modelRobustness wrapper — Monte Carlo uncertainty propagation
المصدر التأسيسيNorris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
الأسماء البديلةMarkov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
ذات صلة50
الملخصA Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.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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ScholarGateقارن الطرق: Markov Model · MONTE-CARLO-SIMULATION. استُرجع بتاريخ 2026-06-17 من https://scholargate.app/ar/compare