Results 1 
2 of
2
Modelling and Analysis of Some Random Process Data from Neurophysiology
"... Models, graphs and networks are particularly useful for examining statistical dependencies amongst quantities via conditioning. In this article the nodal random variables are point processes. Basic to the study of statistical networks is some measure of the strength of (possibly directed) connection ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Models, graphs and networks are particularly useful for examining statistical dependencies amongst quantities via conditioning. In this article the nodal random variables are point processes. Basic to the study of statistical networks is some measure of the strength of (possibly directed) connections between the nodes. The coe#cients of determination and of mutual information are considered in a study for inference concerning statistical graphical models. The focus of this article is simple networks. Both secondorder moment and threshold modelbased analyses are presented. The article includes examples from neurophysiology.
Computing and Information A COMPARATIVE STUDY OF COHERENCE, MUTUAL INFORMATION AND CROSSINTENSITY MODELS
"... Abstract. Coherence is a measure of the time invariant linear dependence of two processes at certain frequencies, and provides a measure of the degree of linear predictability of one process from another process. The coherence is inadequate as a measure of general association for it may be identical ..."
Abstract
 Add to MetaCart
Abstract. Coherence is a measure of the time invariant linear dependence of two processes at certain frequencies, and provides a measure of the degree of linear predictability of one process from another process. The coherence is inadequate as a measure of general association for it may be identically 0 when two series are in fact related. However, such behavior does not occur for the coefficient of mutual information, which is a measure of the amount of information that one random variable contains about another random variable. The LinLin model, which describes the influence of an input on a point process output, can identify linear causal relationships between one sequence of events and another. This paper presents a comparative study of the three approaches using a case study of the relationship between groundwater level data from Tangshan Well and global earthquakes with minimum magnitude 5.8.