Provides an estimation method for blossom tree graphical models. Blossom tree graphical models combine the ideas behind trees and Gaussian graphical models to form a new nonparametric family of graphical models. The approach is to attach nonparanormal blossoms, with arbitrary graphs, to a collection of nonparametric trees. The tree edges are chosen to connect variables that most violate joint Gaussianity. The non-tree edges are partitioned into disjoint groups, and assigned to tree nodes using a nonparametric partial correlation statistic. A nonparanormal blossom is then grown for each group using established methods based on the graphical lasso. The result is a factorization with respect to the union of the tree branches and blossoms, defining a high-dimensional joint density that can be efficiently estimated and evaluated on test points.
Package details |
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Author | Zhe Liu <zheliu@uchicago.edu> |
Maintainer | Zhe Liu <zheliu@uchicago.edu> |
License | GPL-3 |
Version | 0.1.0 |
Package repository | View on GitHub |
Installation |
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