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- W2259919669 abstract "We consider the problem of inferring the conditional independence graph (CIG) of a multivariate stationary dicrete-time Gaussian random process based on a finite length observation. Using information-theoretic methods, we derive a lower bound on the error probability of any learning scheme for the underlying process CIG. This bound, in turn, yields a minimum required sample-size which is necessary for any algorithm regardless of its computational complexity, to reliably select the true underlying CIG. Furthermore, by analysis of a simple selection scheme, we show that the information-theoretic limits can be achieved for a subclass of processes having sparse CIG. We do not assume a parametric model for the observed process, but require it to have a sufficiently smooth spectral density matrix (SDM)." @default.
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- W2259919669 date "2014-03-04" @default.
- W2259919669 modified "2023-09-27" @default.
- W2259919669 title "On the Information-theoretic Limits of Graphical Model Selection for Gaussian Time Series" @default.
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