Managing uncertain data using probabilistic frameworks has attracted much interest lately in
the database literature, and a central computational challenge is probabilistic inference. This …

Existing solutions for probabilistic inference queries mainly focus on answering a single
inference query, but seldom address the issues of efficiently returning results for a sequence …

Hierarchical Markov networks (HMNs) were recently proposed as a faithful representation of
Bayesian networks. We propose a query processing algorithm for HMNs. This method takes …

Bayesian networks have been well established as an effective framework for uncertainty
management using probability. Various methods for probabilistic reasoning in Bayesian …

This chapter is a contribution to the study of formal ontologies. It addresses the problem of
atypical entities in ontologies. The authors propose a new model of knowledge …

Recent studies have adapted existing techniques in population genetics to perform
evolutionary analysis of gene expression (Fay and Wittkopp, 2007; Gu, 2004; Oakley et al …

Abstract Hierarchical Markov networks (HMNs) were recently proposed as a new
representation of Bayesian networks (BNs). HMNs are very important as they possess many …

We identify a broad class of aggregate queries, called MPF queries, inspired by the literature
on marginalizing product functions. MPF queries operate on" functional relations," where a …

Bayesian networks are widely used for knowledge representation and uncertain reasoning.
One of the most important services which Bayesian networks provide is (probabilistic) …