विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्टोकेस्टिक डायनामिक प्रोग्रामिंग× | मोंटे कार्लो सिमुलेशन× | |
|---|---|---|
| क्षेत्र≠ | अनुकरण | निर्णयन |
| परिवार≠ | Process / pipeline | MCDM |
| उद्भव वर्ष≠ | 1957 | 1949 |
| प्रवर्तक≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | Metropolis, N., Ulam, S. |
| प्रकार≠ | Sequential optimization under uncertainty | Robustness wrapper — Monte Carlo uncertainty propagation |
| मौलिक स्रोत≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| उपनाम≠ | SDP, Markov Decision Process, MDP, Stochastic DP | — |
| संबंधित≠ | 6 | 0 |
| सारांश≠ | 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. | 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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