Covariance prediction via convex optimization

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