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	Model średniej ruchomej (MA) w ujęciu bayesowskim	Model bayesowski ARMA
Dziedzina	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	1970s–1997	1970s–1980s
Twórca	Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s
Typ	Bayesian time series model	Bayesian time series model
Źródło pierwotne	West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.
Inne nazwy	Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference
Pokrewne	6	6
Podsumowanie	The Bayesian MA model estimates a moving average time series model within a fully Bayesian framework, placing prior distributions on the MA parameters and error variance and updating them via Bayes' theorem. This approach yields full posterior distributions over model parameters and produces probabilistic forecasts with coherent uncertainty quantification.	The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.