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Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application
A. P. Dawid
Vol. 68, No. 1 (Apr., 1981), pp. 265-274
Stable URL: http://www.jstor.org/stable/2335827
Page Count: 10
You can always find the topics here!Topics: Matrices, Distributivity, T distribution, Integers, Dickeys, Eigenvalues, Linear models, Degrees of freedom, Marginalization, Mathematical vectors
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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.
Biometrika © 1981 Biometrika Trust