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	बायेसियन एसएआरआईएमए मॉडल	बायेसियन VAR मॉडल (BVAR)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1970s–1990s	1984
प्रवर्तक	Box & Jenkins (classical SARIMA); Bayesian extensions developed through Zellner, Geweke, and later MCMC-era researchers	Doan, Litterman & Sims
प्रकार	Bayesian time-series model	Multivariate time-series model
मौलिक स्रोत	Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
उपनाम	Bayesian SARIMA, Bayesian seasonal ARIMA, BSARIMA, Bayesian seasonal time-series model	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
संबंधित	4	5
सारांश	The Bayesian SARIMA model combines the classical Box-Jenkins Seasonal ARIMA framework with Bayesian inference to handle seasonal time-series data. Rather than producing a single point estimate, it yields a full posterior distribution over model parameters, propagating parameter uncertainty directly into forecasts and enabling principled incorporation of prior knowledge.	The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.