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Programació Dinàmica Estocàstica×Simulació Monte Carlo×
CampSimulacióPresa de decisions
FamíliaProcess / pipelineMCDM
Any d'origen19571949
Autor originalBellman, R.; formalized for stochastic settings by Puterman, M. L.Metropolis, N., Ulam, S.
TipusSequential optimization under uncertaintyRobustness wrapper — Monte Carlo uncertainty propagation
Font seminalBellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
ÀliesSDP, Markov Decision Process, MDP, Stochastic DP
Relacionats60
ResumStochastic 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.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.
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ScholarGateCompara mètodes: Stochastic Dynamic Programming · MONTE-CARLO-SIMULATION. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare