Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application
提出并论证了一种方便的矩阵变量分布记法，强调关键参数和理论基础，简化了分布操作，并应用于复合矩阵分布和多元线性模型的贝叶斯预测。
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.