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| Байесовско моделиране, базирано на агенти× | Монте Карло симулация× | |
|---|---|---|
| Област≠ | Симулационно моделиране | Вземане на решения |
| Семейство≠ | Process / pipeline | MCDM |
| Година на възникване≠ | 2000s–2010s | 1949 |
| Създател≠ | Sunnaker et al. / Grazzini & Richiardi (among key contributors) | Metropolis, N., Ulam, S. |
| Тип≠ | Simulation calibration and inference framework | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагащ източник≠ | Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Други названия≠ | Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation | — |
| Свързани≠ | 5 | 0 |
| Резюме≠ | Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations. | 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 от данни ↗ |
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