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The availability of some prior information, along with the current, may help us to improve the properties of statistical techniques. In this study, Bayesian best linear predictor is derived for simple and multivariate calibration situations. A comparative study of the mean squared errors of the Bayesian best linear predictor and the best linear predictor (classical) shows that Bayesian best linear predictor performs equally well. Interval estimates, both for known and unknown parameters, of the best linear predictor have been considered using different pivotal functions and different distributions forThe outcomes have shown that the error probabilities depend uponand to some extent onthe same invariants upon which the mean squared error of the estimator depends.

On best linear and Bayesian linear predictor in calibration