On the Sample Complexity of Graphical Model Selection from Non-Stationary Samples

Abstract

We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed samples are modeled as a zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This includes the case where observations form stationary or underspread processes. We derive a sufficient condition on the required sample size by analyzing a simple sparse neighborhood regression method.