Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовское усреднение моделей временных рядов× | Байесовский вывод для временных рядов× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1999–2010 | 1989 |
| Автор метода≠ | Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions | Mike West and Jeff Harrison |
| Тип≠ | Bayesian ensemble / model combination | Bayesian probabilistic model |
| Основополагающий источник≠ | Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401. link ↗ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 |
| Другие названия | TS-BMA, Bayesian model averaging for time series, BMA forecasting, time series BMA | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Time series Bayesian model averaging (TS-BMA) combines forecasts from an ensemble of time series models — such as AR, VAR, or state-space specifications — by weighting each model by its posterior probability given observed data. Rather than selecting one model and discarding uncertainty about which model is best, TS-BMA integrates over model uncertainty, producing forecasts that are more robust and better calibrated than any single model alone. | Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks. |
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