SECOND-ORDER MOMENTS AND MUTUAL INFORMATION IN THE ANALYSIS OF TIME SERIES

A statistical network is a collection of nodes representing random variables and a set of edges that connect the nodes. A probabilistic model for such is called a statistical graphical model. These models, graphs and networks are particularly useful for examining statistical dependencies amongst quantities via conditioning. In this article the nodal random variables are time series. Basic to the study of statistical networks is some measure of the strength of (possibly directed) connections between the nodes. The use of the ordinary and partial coherences and of mutual information is considered as a study for inference concerning statistical graphical models. The focus of this article is simple networks. The article includes an example from hydrology.

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