Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
This paper investigates relative precision and optimality of analyses for concurrent probabilistic systems. Aiming at the problem at the heart of probabilistic model checking ? com...
Lattice gas cellular automata (LGCA) models provide a relatively fast means of simulating fluid flow and can give both quantitative and qualitative insights into flow patterns aro...
Mitchel Johnson, Daniel P. Playne, Kenneth A. Hawi...
The ability to flexibly compose confidence computation with the operations of relational algebra is an important feature of probabilistic database query languages. Computing confi...