Structure Learning with Nonparametric Decomposable Models

Abstract. We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be handled in a unified framework. Also, consistency of the underlying probabilistic model is guaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data. Preprint, full paper appears in: J. Marques de S´a et al. (Eds.), ICANN 2007, Lecture Notes in Computer Science 4668, pages 119-128. Springer Verlag, 2007. (C) Springer Verlag

• Type: Conference
• Year: 2007
• Where: ICANN
• Authors: Anton Schwaighofer, Mathäus Dejori, Volker Tresp, Martin Stetter
• Keywords: Decomposable Models, ICANN 2007, Springer Verlag, Structure Learning Approach