covariation_matrix: Co-variation matrices

In bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

Co-variation matrices

Description

Construction of model-preserving co-variation matrices for objects of class CI.

Usage

total_covar_matrix(ci, entry, delta)

col_covar_matrix(ci, entry, delta)

partial_covar_matrix(ci, entry, delta)

row_covar_matrix(ci, entry, delta)

Arguments

ci	object of class CI.
entry	a vector of length two specifying the entry of the covariance matrix to vary.
delta	multiplicative variation coefficient for the entry of the covariance matrix given in entry.

Details

Functions to compute total, partial, row-based and column-based co-variation matrices to ensure the conditional independences of the original Bayesian network hold after a variation. If no co-variation is required for model-preservation the functions return a matrix filled with ones (no co-variation).

Value

A co-variation matrix of the same size of the covariance matrix of CI.

References

C. Görgen & M. Leonelli (2020), Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 1-32.

See Also

model_pres_cov

Examples

total_covar_matrix(synthetic_ci,c(1,1),0.3)
total_covar_matrix(synthetic_ci,c(1,2),0.3)
partial_covar_matrix(synthetic_ci,c(1,2),0.3)
row_covar_matrix(synthetic_ci,c(1,2),0.3)
col_covar_matrix(synthetic_ci,c(1,2),0.3)

bnmonitor documentation built on June 7, 2023, 5:19 p.m.