השוואת שיטות

	מודל מרקוב	סימולציית מונטה קרלו
תחום	סימולציה	קבלת החלטות
משפחה	Process / pipeline	MCDM
שנת המקור	1906	1949
הוגה השיטה	Andrei Markov	Metropolis, N., Ulam, S.
סוג	Probabilistic state-transition model	Robustness wrapper — Monte Carlo uncertainty propagation
מקור מכונן	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
כינויים	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process	—
קשורות	5	0
תקציר	A 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.