The learning of probabilistic models with many hidden variables and nondecomposable dependencies is an important and challenging problem. In contrast to traditional approaches bas...
— In many applications of failure time data analysis, it is important to perform inferences about the median of the distribution function in situations of failure time data model...
We introduce a framework for assessing the effectiveness of partial evaluators in functional logic languages. Our framework is based on properties of the rewrite system that models...
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
We address schema design in uncertain databases. Since uncertain data is relational in nature, decomposition becomes a key issue in design. Decomposition relies on dependency theo...
Anish Das Sarma, Jeffrey D. Ullman, Jennifer Widom