Linganisha mbinu

	Muundo Imara wa Markov	Uiguzi wa Monte Carlo
Nyanja	Uigaji	Ufanyaji Maamuzi
Familia	Process / pipeline	MCDM
Mwaka wa asili	2005	1949
Mwanzilishi	Nilim & El Ghaoui; Iyengar	Metropolis, N., Ulam, S.
Aina	Robust probabilistic model	Robustness wrapper — Monte Carlo uncertainty propagation
Chanzo asilia	Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Majina mbadala	RMM, Robust Markov Chain, Uncertain Markov Model, Interval Markov Model	—
Zinazohusiana	4	0
Muhtasari	A Robust Markov Model applies robustness principles to Markov chains by replacing single-point transition probabilities with uncertainty sets, then optimizing against the worst-case realization. Originally developed for robust Markov decision processes in operations research, it is used wherever transition rates are estimated with noise or are subject to adversarial variation, ensuring decisions remain safe across the full uncertainty range.	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.