We consider the problem of constructing nonparametric undirected graphical models for high-dimensional functional data. Most existing statistical methods in this context assume either a Gaussian distribution on vertices or linear conditional means. In article, we provide more flexible model which relaxes linearity assumption by replacing it an arbitrary additive form. The use principal componen...

Learning the structure of dependence relations between variables is a pervasive issue in statistical literature. A directed acyclic graph (DAG) can represent set conditional independencies, but different DAGs may encode same and are indistinguishable using observational data. Equivalent be collected into classes, each represented by partially known as essential (EG). Structure learning directly...