Salīdzināt metodes

	Stohastiskais Markova modelis	Monte Carlo simulācija
Nozare	Simulācija	Lēmumu pieņemšana
Saime	Process / pipeline	MCDM
Izcelsmes gads	1993	1949
Autors	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)	Metropolis, N., Ulam, S.
Tips	Probabilistic state-transition model with Monte Carlo uncertainty propagation	Robustness wrapper — Monte Carlo uncertainty propagation
Pirmavots	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
Citi nosaukumi	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model	—
Saistītās	6	0
Kopsavilkums	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.