Sammenlign metoder

	Tidsserie Bayesiansk Modelgennemsnit	Bayesiansk regression
Fagområde	Bayesiansk	Bayesiansk
Familie	Bayesian methods	Bayesian methods
Oprindelsesår	1999–2010	—
Ophavsperson	Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions	—
Type	Bayesian ensemble / model combination	Bayesian linear model
Oprindelig kilde	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Aliasser	TS-BMA, Bayesian model averaging for time series, BMA forecasting, time series BMA	bayesian linear regression, probabilistic regression, bayesian regresyon
Relaterede	5	2
Resumé	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 regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.