Comparar métodos

	Modelo de Markov estocástico	Programación Dinámica Estocástica
Campo	Simulación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	1993	1957
Autor original	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
Tipo	Probabilistic state-transition model with Monte Carlo uncertainty propagation	Sequential optimization under uncertainty
Fuente seminal	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
Alias	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model	SDP, Markov Decision Process, MDP, Stochastic DP
Relacionados	6	6
Resumen	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.	Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.