diameter {bnmonitor} | R Documentation |
Diameters in a Bayesian network
Description
Computation of the diameters of all conditional probability tables in a Bayesian network.
Usage
diameter(bnfit)
Arguments
bnfit |
object of class |
Details
The diameter of a conditional probability table P
with n
rows p_1,\dots,p_n
is
d^+(P)=\max_{i,j\leq n} d_V(p_i,p_j),
where d_V
is the total variation distance between two probability mass functions over a sample space \mathcal{X}
, i.e.
d_V(p_i,p_j)=\frac{1}{2}\sum_{x\in\mathcal{X}}|p_i(x)-p_j(x)|.
Value
A dataframe with the following columns: Nodes
- the vertices of the BN; Diameter
- the diameters of the associated conditional probability tables.
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.
Examples
diameter(travel)
[Package bnmonitor version 0.2.0 Index]