This paper presents a complete theory of credal networks, structures that associate convex
sets of probability measures with directed acyclic graphs. Credal networks are graphical …

Data are being generated and used to support all aspects of healthcare provision, from
policy formation to the delivery of primary care services. Particularly, with the change of …

Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …

There has been a long standing division in AI between logical symbolic and probabilistic
reasoning approaches. While probabilistic models can deal well with inherent uncertainty in …

Naive Bayes classifiers provide an efficient and scalable approach to supervised
classification problems. When some entries in the training set are missing, methods exist to …

Bayesian Network (BN) is often criticized for demanding a large number of
crisp/exact/precise conditional probability numbers which, due to the lack of statistics, have …

Safety risk assessment of metro construction is necessary to prevent catastrophic accidents
that may cause heavy financial losses and casualties. In this paper, we introduce a …

This paper presents an overview of graphical models that can handle imprecision in
probability values. The paper first reviews basic concepts and presents a brief historical …

Most probabilistic inference algorithms are specified and processed on a propositional level,
even though many domains are better represented by first-order specifications that …

Bayesian network (BN) is an effective tool for causal inferences of accidents. However, it is
often criticized for the difficulty in obtaining accurate/sufficient data needed to get precise …