Comparer des méthodes

	Modèle de Markov stochastique	Analyse de sensibilité
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1993	2004
Auteur d'origine	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Type	Probabilistic state-transition model with Monte Carlo uncertainty propagation	Robustness wrapper — parameter / weight perturbation sensitivity indices
Source fondatrice	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester
Alias	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model	—
Apparentées	6	0
Résumé	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.	SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.