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

Volume: 68, Pages: 17 - 32
Published: Jan 1, 2020
Abstract
We study conditions that allow accurate graphical model selection from non-stationary data. The observed data is modelled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting of i.i.d. samples as well as the case of samples forming a stationary time series. More generally, our approach applies to any data for which...
Paper Details
Title
On the Sample Complexity of Graphical Model Selection From Non-Stationary Samples
Published Date
Jan 1, 2020
Volume
68
Pages
17 - 32
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