  Bayesian inference is developed for matrix-variate dynamic linear models
(MV-DLMs), in order to allow missing observation analysis, of any sub-vector or
sub-matrix of the observation time series matrix. We propose modifications of
the inverted Wishart and matrix $t$ distributions, replacing the scalar degrees
of freedom by a diagonal matrix of degrees of freedom. The MV-DLM is then
re-defined and modifications of the updating algorithm for missing observations
are suggested.
