Sparse Estimation of Conditional Graphical Models With Application to Gene Networks.

In many applications the graph structure in a network arises from two sources: intrinsic connections and connections due to external effects. We introduce a sparse estimation procedure for graphical models that is capable of isolating the intrinsic connections by removing the external effects. Technically, this is formulated as a graphical model, in which the external effects are modeled as predictors, and the graph is determined by the conditional precision matrix.