Compute the maximum likelihood estimate of the precision matrix, given a known graphical structure (i.e., an adjacency matrix). This approach was originally described in "The Elements of Statistical Learning" \insertCite@see pg. 631, @hastie2009elementsGGMnonreg.
An adjacency matrix that encodes the constraints, where a zero indicates that element should be zero.
A list containing the following:
Theta: Inverse of the covariance matrix (precision matrix), that encodes the conditional (in)dependence structure.
Sigma: Covariance matrix.
wadj: Weighted adjacency matrix, corresponding to the partial correlation network.
The algorithm is written in
c++, and should scale to high dimensions.
Note there are a variety of algorithms for this purpose. Simulation studies indicated that this approach is both accurate and computationally efficient \insertCite@HFT therein, @emmert2019constrainedGGMnonreg
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