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Stochastic Dynamic Programming×Simulacija Monte Karlo×
OblastSimulacijaDonošenje odluka
PorodicaProcess / pipelineMCDM
Godina nastanka19571949
TvoracBellman, R.; formalized for stochastic settings by Puterman, M. L.Metropolis, N., Ulam, S.
TipSequential optimization under uncertaintyRobustness wrapper — Monte Carlo uncertainty propagation
Temeljni izvorBellman, 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 ↗
Drugi naziviSDP, Markov Decision Process, MDP, Stochastic DP
Srodne60
SažetakStochastic 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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ScholarGateUporedite metode: Stochastic Dynamic Programming · MONTE-CARLO-SIMULATION. Preuzeto 2026-06-15 sa https://scholargate.app/sr/compare