In a bayes net context consider the following covariance matrix where G is the child node and D and E are continuous parents
G D E G 11.4038771 0.8944238 9.3671434 D 0.8944238 0.2937604 0.2322106 E 9.3671434 0.2322106 8.8262010
36.6174413683 #mean of G [7.35806706, 15.06258672] #mean of D and E respectively
I know that the equation used is
μa|b = μa +Σab Σbb−1(xb −μb)
But I am just having some trouble to compute the conditional linear gaussian mean with more than one continuous parent, so please someone show how to compute it with some clear steps.
Notice: this is the clgaussian.test dataset from bnlearn R library.