  We consider the problem of predicting the covariance of a zero mean Gaussian
vector, based on another feature vector. We describe a covariance predictor
that has the form of a generalized linear model, i.e., an affine function of
the features followed by an inverse link function that maps vectors to
symmetric positive definite matrices. The log-likelihood is a concave function
of the predictor parameters, so fitting the predictor involves convex
optimization. Such predictors can be combined with others, or recursively
applied to improve performance.
