Сравнение на методи

	Байесов модел на авторегресия (AR)	Байесов модел ARIMA
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1971	1970s (ARIMA); Bayesian extension prominent from 1990s
Създател	Arnold Zellner; foundational Bayesian time-series work by West & Harrison	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)
Тип	Bayesian time-series model	Bayesian time series model
Основополагащ източник	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903
Други названия	Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model
Свързани	6	6
Резюме	The Bayesian AR model estimates an autoregressive time-series process by combining a likelihood derived from the AR structure with prior distributions over the lag coefficients and error variance. Rather than producing single point estimates, it yields full posterior distributions, enabling principled uncertainty quantification and probabilistic forecasting.	The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting.