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	Bayesowski model Markowa	Symulacja Monte Carlo
Dziedzina	Symulacja	Podejmowanie decyzji
Rodzina	Process / pipeline	MCDM
Rok powstania	1990s–2000s	1949
Twórca	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community	Metropolis, N., Ulam, S.
Typ	Probabilistic state-transition simulation	Robustness wrapper — Monte Carlo uncertainty propagation
Źródło pierwotne	Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Inne nazwy	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation	—
Pokrewne	4	0
Podsumowanie	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.	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.