방법 비교

	동적 베이즈 모델 평균화	Bayesian Model Averaging
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	2010	1999
창시자	Raftery, Karny & Ettler	Hoeting, Madigan, Raftery & Volinsky
유형	dynamic ensemble / model combination	Bayesian model averaging
원전	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.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
별칭	DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
관련	6	5
요약	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.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.