Hybrid Bayesian networks have received an increasing attention during the last years. The
difference with respect to standard Bayesian networks is that they can host discrete and …

When clustering a dataset, the right number k of clusters to use is often not obvious, and
choosing k automatically is a hard algorithmic problem. In this paper we present an …

In this paper we propose the use of mixtures of truncated exponential (MTE) distributions in
hybrid Bayesian networks. We study the properties of the MTE distribution and show how …

ABSTRACT Student dropout in Engineering Education is an important problem which has
been studied from different perspectives, as well as using different techniques. This …

Loopy belief propagation (BP) has been successfully used in a number of difficult graphical
models to find the most probable configuration of the hidden variables. In applications …

We adopt probabilistic decision graphs developed in the field of automated verification as a
tool for probabilistic model representation and inference. We show that probabilistic …

Abstract The MTE (Mixture of Truncated Exponentials) model allows to deal with Bayesian
networks containing discrete and continuous variables simultaneously. This model offers an …

Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when
working with hybrid Bayesian networks. One of the features of the MTE model is that …

This paper presents an approximate algorithm to obtain a posteriori intervals of probability,
when available information is also given with intervals. The algorithm uses probability trees …

When a Bayesian network (BN) is modified, for example adding or deleting a node, or
changing the probability distributions, we usually will need a total recompilation of the …