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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Schwaighofer, A., Dejori, M., Tresp, V., & Stetter, M. (2007). Structure learning with nonparametric decomposable models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4668 LNCS, pp. 119–128). Springer Verlag. https://doi.org/10.1007/978-3-540-74690-4_13
Mendeley helps you to discover research relevant for your work.