Accession Number:

ADA218473

Title:

Bayesian Nonparametric Prediction and Statistical Inference

Descriptive Note:

Final rept. 1 Apr 1987-31 Mar 1989,

Corporate Author:

MICHIGAN UNIV ANN ARBOR DEPT OF STATISTICS

Personal Author(s):

Report Date:

1989-09-07

Pagination or Media Count:

29.0

Abstract:

The problem of Bayesian nonparametric prediction and statistical inference is formulated and discussed. A solution is proposed based upon A sub n and H sub n as in Hill 1968. The meaning of parameters in the subjective Bayesian theory of Bruno de Finetti is discussed in connection both with A sub n and with conventional parametric models. It is argued that the usual sharp distinction between prediction and parametric inference is largely illusory. The finite version of de Finettis theorem is emphasized for the practice of statistics, with the infinite case used only to obtain approximations and insight. kr

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE