Bayes linear adjustment for variance matrices
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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. The 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.
To appear in the Bayesian Statistics 5 conference volume. LaTeX, 11 pages, Chicago BIB-style (included), 2 postscript figures. Also available as a postscript file from http://fourier.dur.ac.uk:8000/~dma3djw/djwgvar.html For information on [B/D], go to http://fourier.dur.ac.uk:8000/stats/bd/
To appear in the Bayesian Statistics 5 conference volume. LaTeX, 11 pages, Chicago BIB-style (included), 2 postscript figures. Also available as a postscript file from http://fourier.dur.ac.uk:8000/~dma3djw/djwgvar.html For information on [B/D], go to http://fourier.dur.ac.uk:8000/stats/bd/