Comparer des méthodes

	Modèle de Markov bayésien	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	1906
Auteur d'origine	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community	Andrei Markov
Type	Probabilistic state-transition simulation	Probabilistic state-transition model
Source fondatrice	Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Alias	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	4	5
Résumé	A Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yielding posterior distributions over outcomes such as costs, life-years, or quality-adjusted life-years rather than single-point estimates.	A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.