KL_bounds {bnmonitor} | R Documentation |
Bounds for the KL-divergence
Description
Computation of the bounds of the KL-divergence for variations of each parameter of a CI
object.
Usage
KL_bounds(ci, delta)
Arguments
ci |
object of class |
delta |
multiplicative variation coefficient for the entry of the covariance matrix given in |
Details
Let \Sigma
be the covariance matrix of a Gaussian Bayesian network with n
vertices.
Let D
and \Delta
be variation matrices acting additively on \Sigma
. Let also \tilde\Delta
be a model-preserving co-variation matrix.
Denote with Y
and \tilde{Y}
the original and the perturbed random vectors. Then for a standard sensitivity analysis
KL(\tilde{Y}||Y)\leq 0.5n\max\left\{f(\lambda_{\max}(D\Sigma^{-1})),f(\lambda_{\min}(D\Sigma^{-1}))\right\}
whilst for a model-preserving one
KL(\tilde{Y}||Y)\leq 0.5n\max\left\{f(\lambda_{\max}(\tilde\Delta\circ\Delta)),f(\lambda_{\min}(\tilde\Delta\circ\Delta))\right\}
where \lambda_{\max}
and \lambda_{\min}
are the largest and the smallest eigenvalues, respectively, f(x)=\ln(1+x)-x/(1+x)
and \circ
denotes the Schur or element-wise product.
Value
A dataframe including the KL-divergence bound for each co-variation scheme (model-preserving and standard) and every entry of the covariance matrix. For variations leading to non-positive semidefinite matrix, the dataframe includes a NA
.
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
Examples
KL_bounds(synthetic_ci,1.05)