View source: R/model_pres_cov.R
model_pres_cov | R Documentation |
Model-preserving co-variation for objects of class CI
.
model_pres_cov(ci, type, entry, delta)
ci |
object of class |
type |
character string. Type of model-preserving co-variation: either |
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 |
Let the original Bayesian network have a Normal distribution \mathcal{N}(\mu,\Sigma)
and let entry
be equal to (i,j)
. For a multiplicative variation of the covariance matrix by an amount \delta
, a variation matrix \Delta
is constructed as
\Delta_{k,l}=\left\{
\begin{array}{ll}
\delta & \mbox{if } k=i, l=j\\
\delta & \mbox{if } l=i, k=j \\
0 & \mbox{otherwise}
\end{array}
\right.
A co-variation matrix \tilde\Delta
is then constructed and the resulting distribution after the variation is \mathcal{N}(\mu,\tilde\Delta\circ\Delta\circ\Sigma)
, assuming \tilde\Delta\circ\Delta\circ\Sigma
is positive semi-definite and where \circ
denotes the Schur (or element-wise) product. The matrix \tilde\Delta
is so constructed to ensure that all conditional independence in the original Bayesian networks are retained after the parameter variation.
If the resulting covariance is positive semi-definite, model_pres_cov
returns an object of class CI
with an updated covariance matrix. Otherwise it returns an object of class npsd.ci
, which has the same components of CI
but also has a warning entry specifying that the covariance matrix is not positive semi-definite.
C. Görgen & M. Leonelli (2020), Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 1-32.
covariance_var
, covariation_matrix
model_pres_cov(synthetic_ci,"partial",c(1,3),1.1)
model_pres_cov(synthetic_ci,"partial",c(1,3),0.9)
model_pres_cov(synthetic_ci,"total",c(1,2),0.5)
model_pres_cov(synthetic_ci,"row",c(1,3),0.98)
model_pres_cov(synthetic_ci,"column",c(1,3),0.98)
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