Using mutual information to determine relevance in Bayesian networks

A study on decision-making of food supply chain based on big data

“…MI (Mutual Information) would be used for sensitivity analysis. MI is a measure of the dependence between two random variables and is more suitable for Bayesian network to sensitivity analysis (Nicholson & Jitnah 1998 …”

Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systems

“…Nicholson and Jitnah [15] and later Ebert-Uphoff [16,17] used mutual information as the basis of the measure of link strength. Lacave [18] proposed a measure of link strength for the purpose of explanation in decision support systems based on Bayesian networks.…”

Guidelines for Managing Sensors in Cyber Physical Systems with Multiple Sensors

“…These measures were first introduced by Boerlag [42] for Bayesian networks with binary nodes (two states). The Connection Strength (CS) measures the strength between any two nodes in the network (without accounting for the path between the two) whereas Link Strength (LS) (also referred to as arc weight [43]) specifically calculates the strength along a particular link between two adjacent nodes.…”

“…Both these ideas were introduced initially as a way to improve the visualization of the network structure when learned from data (e.g., using thicker links to represent stronger relationships) but were also later used to improve the efficiency of inferencing algorithms (e.g., by eliminating links with insignificant weights) [43,44]. CS and LS are based on information theory concepts of entropy and mutual information.…”

“…where P pr is an approximation of the prior probability of the node being in a particular state and is approximated by averaging the conditional probabilities of that node over all its parent state combinations. As a computationally useful approximation, [43] provides another formulation of link strength as:…”