Comparar métodos

	Modelo Bayesiano de Markov	Simulação de Monte Carlo
Área	Simulação	Tomada de decisão
Família	Process / pipeline	MCDM
Ano de origem	1990s–2000s	1949
Autor original	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community	Metropolis, N., Ulam, S.
Tipo	Probabilistic state-transition simulation	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminal	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
Outros nomes	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation	—
Relacionados	4	0
Resumo	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.