dwi: Distance-weigthed influence

In bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

Distance-weigthed influence

Description

Computation of the distance-weigthed influence in a Bayesian network

Usage

dwi(bn, node, w)

Arguments

bn	object of class bn.fit or bn.
node	a node of bnfit.
w	a number in (0,1].

Details

The distance-weigthed influence of a node X_j on an output node X_i in a Bayesian network is

DWI(X_j,X_i,w)= \sum_{s\in S_{ji}}w^{|s|},

where S_{ji} is the set of active trails between X_j and X_i, w\in(0,1] is an input parameter, and |s| is the length of the trail s.

Value

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

References

Albrecht, D., Nicholson, A. E., & Whittle, C. (2014). Structural sensitivity for the knowledge engineering of Bayesian networks. In Probabilistic Graphical Models (pp. 1-16). Springer International Publishing.

See Also

ewi, mutual_info

Examples

dwi(travel, "T", 0.5)

bnmonitor documentation built on Sept. 30, 2024, 9:31 a.m.