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 a...
Abstract -We study the problem of communication reliability and diversity in multi-hop wireless networks. Our aim is to develop a new network model that better takes into account t...
Amir Ehsan Khandani, Jinane Abounadi, Eytan Modian...
Given a parallel parsed corpus, statistical treeto-tree alignment attempts to match nodes in the syntactic trees for a given sentence in two languages. We train a probabilistic tr...
Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The ...
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn ...