Bayes Linear Adjustment for Variance Matrices
David Wilkinson, Michael Goldstein · 1996
Abstract We examine the problem of covariance belief revision using a geometric approach. We exhibit an inner-product space where covariance matrices live naturally—a space of random real symmetric matrices. Toe inner product on this space captures aspects of our beliefs about the relationship between covariance matrices of interest to us, providing a structure rich enough for us to adjust beliefs about unknown matrices in the light of data such as sample covariance matrices, exploiting second-order exchangeability specifications.