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bnmonitor (version 0.2.2)

ewi: Edge-weigthed influence

Description

Computation of the edge-weigthed influence in a Bayesian network

Usage

ewi(bnfit, node)

Value

A dataframe with the following columns: Nodes - the vertices of the BN; Influence - the edge-weigthed influence of the corresponding node.

Arguments

bnfit

object of class bn.fit.

node

a node of bnfit

Details

The edge-weigthed influence of a node \(X_j\) on an output node \(X_i\) in a Bayesian network is $$EWI(X_j,X_i)= \sum_{s\in S_{ji}}\left(\prod_{(k,l)\in s}\delta_{kl}\right)^{|s|},$$ where \(S_{ji}\) is the set of active trails between \(X_j\) and \(X_i\), \(\delta_{kl}\) is the strength of an edge between \(X_k\) and \(X_l\), and \(|s|\) is the length of the trail \(s\).

References

Leonelli, M., Smith, J. Q., & Wright, S. K. (2024). The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks. arXiv preprint arXiv:2407.04667.

See Also

mutual_info, dwi, edge_strength

Examples

Run this code
ewi(travel, "T")

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