psd_check {bnmonitor} | R Documentation |
Check for positive semi-definiteness after a perturbation
Description
psd_check
returns a boolean to determine if the covariance matrix after a perturbation is positive semi-definite.
Usage
psd_check(x, ...)
## S3 method for class 'GBN'
psd_check(x, entry, delta, ...)
## S3 method for class 'CI'
psd_check(x, type, entry, delta, ...)
Arguments
x |
object of class |
... |
additional arguments for compatibility. |
entry |
a vector of length 2 indicating the entry of the covariance matrix to vary. |
delta |
numeric vector, including the variation parameters that act additively. |
type |
character string. Type of model-preserving co-variation: either |
Details
The details depend on the class the method psd_check
is applied to.
Let be the covariance matrix of a Gaussian Bayesian network and let
be a perturbation matrix acting additively. The perturbed covariance matrix
is positive semi-definite if
where is the smallest eigenvalue end
is the spectral radius.
Value
A dataframe including the variations performed and the check for positive semi-definiteness.
Methods (by class)
-
psd_check(GBN)
:psd_check
for objectsGBN
-
psd_check(CI)
:psd_check
for objectsCI
References
C. Görgen & M. Leonelli (2020), Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 1-32.
Examples
psd_check(synthetic_gbn,c(2,4),-3)
psd_check(synthetic_gbn,c(2,3),seq(-1,1,0.1))
psd_check(synthetic_ci,"partial",c(2,4),0.95)
psd_check(synthetic_ci,"all",c(2,3),seq(0.9,1.1,0.01))