Comparar métodos

	Modelo Bayesiano de Markov	Modelo de Markov Estocástico
Área	Simulação	Simulação
Família	Process / pipeline	Process / pipeline
Ano de origem	1990s–2000s	1993
Autor original	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Tipo	Probabilistic state-transition simulation	Probabilistic state-transition model with 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	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
Outros nomes	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Relacionados	4	6
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.	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.