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Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application

A. P. Dawid
Biometrika
Vol. 68, No. 1 (Apr., 1981), pp. 265-274
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2335827
Stable URL: http://www.jstor.org/stable/2335827
Page Count: 10
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application
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Abstract

We introduce and justify a convenient notation for certain matrix-variate distributions which, by its emphasis on the important underlying parameters, and the theory on which it is based, eases greatly the task of manipulating such distributions. Important examples include the matrix-variate normal, t, F and beta, and the Wishart and inverse Wishart distributions. The theory is applied to compound matrix distributions and to Bayesian prediction in the multivariate linear model.

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