Bayesian networks (BNs) model problems that involve uncertainty. A BN is a directed graph,
whose nodes are the uncertain variables and whose edges are the causal or influential links …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

Bayesian networks are annotated directed graphs that encode probabilistic relations among
variables in uncertain-reasoning problems. The variables may be discrete or continuous …

As long as knowledge-based systems have been built, facilities for handling uncertainty
have been an integral part. In the early days of rule-based programming, the predominant …

Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

Reasoning with uncertainty is more common than reasoning without. Based on just a limited
number of observed events we decide to perform an action. However, the events that we …

This paper proposes a systematized presentation and a terminology for observations in a
Bayesian network. It focuses on the three main concepts of uncertain evidence, namely …

Applications in various domains often lead to high dimensional dependence modelling. A
Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of …

Probabilistic graphical models and decision graphs are powerful modeling tools for
reasoning and decision making under uncertainty. As modeling languages they allow a …

Bayesian belief networks provide a natural, efficient method for representing probabilistic
dependencies among a set of variables. For these reasons, numerous researchers are …