We consider the statistical problem of analyzing the association between two categorical variables from cross-classified data. The focus is put on measures which enable one to st...
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
We define the concept of dependence among multiple variables using maximum entropy techniques and introduce a graphical notation to denote the dependencies. Direct inference of in...
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Reasoning with both probabilistic and deterministic dependencies is important for many real-world problems, and in particular for the emerging field of statistical relational lear...