2017
Learning conditional independence structure for high-dimensional uncorrelated vector processes
ICASSP 2017accepted
We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time but having a time-v…