Seminar Series: Graph estimation with matrix variate normal instances
Undirected graphs can be used to describe matrix variate distributions. In this talk, I describe new methods for estimating the graphical structures and underlying parameters, namely, the row and column covariance and inverse covariance matrices from the matrix variate data. Under sparsity conditions, we show that one is able to recover the graphs and covariance matrices with a single random matrix from the matrix variate normal distribution. Consistency and the rates of convergence in the operator and the Frobenius norm will be established.