psd_check {bnmonitor} | R Documentation |
psd_check
returns a boolean to determine if the covariance matrix after a perturbation is positive semi-definite.
psd_check(x, ...) ## S3 method for class 'GBN' psd_check(x, entry, delta, ...) ## S3 method for class 'CI' psd_check(x, type, entry, delta, ...)
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 |
The details depend on the class the method psd_check
is applied to.
Let Σ be the covariance matrix of a Gaussian Bayesian network and let D be a perturbation matrix acting additively. The perturbed covariance matrix Σ+D is positive semi-definite if
ρ(D)≤q λ_{\min}(Σ)
where λ_{\min} is the smallest eigenvalue end ρ is the spectral radius.
A dataframe including the variations performed and the check for positive semi-definiteness.
GBN
: psd_check
for objects GBN
CI
: psd_check
for objects CI
C. GĂ¶rgen & M. Leonelli (2020), Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 1-32.
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))