Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастический сценарный анализ× | Стохастическое динамическое программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1955–1980s | 1957 |
| Автор метода≠ | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Тип≠ | Probabilistic scenario enumeration and evaluation | Sequential optimization under uncertainty |
| Основополагающий источник≠ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Другие названия | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis | SDP, Markov Decision Process, MDP, Stochastic DP |
| Связанные≠ | 4 | 6 |
| Сводка≠ | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. | 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. |
| ScholarGateНабор данных ↗ |
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