Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Системная динамика с байесовским подходом× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Имитационное моделирование | Принятие решений |
| Семейство≠ | Process / pipeline | MCDM |
| Год появления≠ | 2000s–2010s | 1949 |
| Автор метода≠ | Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities | Metropolis, N., Ulam, S. |
| Тип≠ | Simulation with probabilistic parameter learning | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics | — |
| Связанные≠ | 6 | 0 |
| Сводка≠ | Bayesian System Dynamics (BSD) integrates Bayesian statistical inference with causal stock-and-flow simulation models. Prior knowledge about model parameters is updated using observed time-series data to produce posterior distributions, which are then propagated through the simulation to yield probabilistic forecasts and policy evaluations rather than single deterministic trajectories. | 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. |
| ScholarGateНабор данных ↗ |
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