방법 비교

	시계열 베이지안 모형 평균	Bayesian Model Averaging
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1999–2010	1999
창시자	Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions	Hoeting, Madigan, Raftery & Volinsky
유형	Bayesian ensemble / model combination	Bayesian model averaging
원전	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
별칭	TS-BMA, Bayesian model averaging for time series, BMA forecasting, time series BMA	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
관련	5	5
요약	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.	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.