Nonconjugate Bayesian regression on many variables

In this paper the problem of normal linear regression on arbitrarily many variables is considered. It is shown that a nonconjugate prior implies indeterminism for the Bayes predictor, in contrast to determinism induced by the use of the usual conjugate priors. A comparison is made between the Bayes predictor and a sample-based least squares predictor which fits the data perfectly. (C) 2002 Published by Elsevier Science B.V.