Сравнение методов

	Динамическое байесовское усреднение моделей	Динамический байесовский вывод
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	2010	1989–1997
Автор метода	Raftery, Karny & Ettler	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Тип	dynamic ensemble / model combination	Bayesian sequential / online inference framework
Основополагающий источник	Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Другие названия	DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Связанные	6	6
Сводка	Dynamic Bayesian Model Averaging (DMA) extends standard Bayesian model averaging to settings where the best predictive model may change over time. It maintains a probability distribution over a set of competing models and updates that distribution sequentially as new observations arrive, allowing model weights to evolve rather than remaining fixed across the entire sample.	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.