Módszerek összehasonlítása

	Sztochasztikus Markov-modell	MONTE-CARLO-SIMULATION
Tudományterület	Szimuláció	Döntéshozatal
Módszercsalád	Process / pipeline	MCDM
Keletkezés éve	1993	1949
Megalkotó	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)	Metropolis, N., Ulam, S.
Típus	Probabilistic state-transition model with Monte Carlo uncertainty propagation	Robustness wrapper — Monte Carlo uncertainty propagation
Alapmű	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alternatív nevek	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model	—
Kapcsolódó	6	0
Összefoglaló	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.	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.