Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Resolution refinements called w-resolution trees with lemmas (WRTL) and with input lemmas (WRTI) are introduced. Dag-like resolution is equivalent to both WRTL and WRTI when there...
Finite-domain constraint programming has been used with great success to tackle a wide variety of combinatorial problems in industry and academia. To apply finite-domain constrain...
Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan, Ian...
In recent years, there has been a growing interest in applying Bayesian networks and their extensions to reconstruct regulatory networks from gene expression data. Since the gene ...