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Stochastické dynamické programovanie×Simulácia Monte Carlo×
OdborSimuláciaRozhodovanie
RodinaProcess / pipelineMCDM
Rok vzniku19571949
TvorcaBellman, R.; formalized for stochastic settings by Puterman, M. L.Metropolis, N., Ulam, S.
TypSequential optimization under uncertaintyRobustness wrapper — Monte Carlo uncertainty propagation
Pôvodný zdrojBellman, 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 ↗
Ďalšie názvySDP, Markov Decision Process, MDP, Stochastic DP
Príbuzné60
ZhrnutieStochastic 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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ScholarGatePorovnať metódy: Stochastic Dynamic Programming · MONTE-CARLO-SIMULATION. Získané 2026-06-15 z https://scholargate.app/sk/compare