Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and
integrate both quantitative and qualitative data, thus accommodating data-limited conditions …

Bayesian networks are a type of probabilistic graphical models lie at the intersection
between statistics and machine learning. They have been shown to be powerful tools to …

While all kinds of mixed data---from personal data, over panel and scientific data, to public
and commercial data---are collected and stored, building probabilistic graphical models for …

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 …

We introduce semiparametric Bayesian networks that combine parametric and
nonparametric conditional probability distributions. Their aim is to incorporate the …

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 …

Traditional estimation of distribution algorithms (EDAs) often use Gaussian densities to
optimize continuous functions, such as the estimation of Gaussian network algorithms …

This paper presents a new class of Bayesian networks called hybrid semiparametric
Bayesian networks, which can model hybrid data (discrete and continuous data) by mixing …

Bayesian networks (BNs) facilitate the establishment and communication of complex and
large probabilistic models that are best characterized through local dependences and …

Bayesian networks (BNs) have become a standard in the field of Artificial Intelligence as a
means of dealing with uncertainty and risk modelling. In recent years, there has been …