期刊
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
卷 103, 期 1-2, 页码 245-261出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/S0378-3758(01)00224-5
关键词
regression; Bayesian inference; nonconjugate prior; indeterminism
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.
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