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	Programmazione Dinamica Bayesiana	Modello Markoviano Bayesiano
Campo	Simulazione	Simulazione
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1957 (Bellman DP); Bayesian extensions 1990s–2000s	1990s–2000s
Ideatore	Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community
Tipo	Sequential optimization with Bayesian belief updating	Probabilistic state-transition simulation
Fonte seminale	Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267	Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629
Alias	BDP, Bayesian DP, Bayesian sequential optimization, Bayesian stochastic control	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation
Correlati	4	4
Sintesi	Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal policy that explicitly accounts for both immediate rewards and the value of information gained through exploration.	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.