So sánh phương pháp

	Trung bình mô hình Bayes động	Suy luận Bayes động
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	2010	1989–1997
Người khởi xướng	Raftery, Karny & Ettler	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Loại	dynamic ensemble / model combination	Bayesian sequential / online inference framework
Công trình gốc	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
Tên gọi khác	DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Liên quan	6	6
Tóm tắt	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.