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Bayesian Markov Model

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.

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Bayesian Markov Model — State-Transition Modeling with Bayesian Parameter Estimation

Taxonomisk metodpost · process-pipeline / simulation

Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. · ISBN 9780198526629
Jackson, C. H., Sharples, L. D., Thompson, S. G. (2010). Structural and parameter uncertainty in Bayesian cost-effectiveness models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(2), 233-253. · DOI 10.1111/j.1467-9876.2009.00684.x

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