Сравнение методов

	Робастная модель Маркова	Стохастическая марковская модель
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2005	1993
Автор метода	Nilim & El Ghaoui; Iyengar	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Тип	Robust probabilistic model	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Основополагающий источник	Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798.	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
Другие названия	RMM, Robust Markov Chain, Uncertain Markov Model, Interval Markov Model	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Связанные	4	6
Сводка	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.	A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.