Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, dep...
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation," unifies two previous techniques: assumed-de...
For extracting the characteristics a specific geographic entity, and notably a place, we propose to use dynamic Extreme Tagging Systems in combination with the classic approach of...
: Previously we have proposed a theoretical framework, called BayesOWL, to model uncertainty in semantic web ontologies based on Bayesian networks. In particular, we have developed...