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 …

The representation and quantification of uncertainty has received increasing attention in
machine learning in the recent past. The formalism of credal sets provides an interesting …

Neighbourhoods of precise probabilities are instrumental to perform robustness analysis, as
they rely on very few parameters. Many such models, sometimes referred to as distortion …

Supervised hyperspectral image (HSI) classification relies on accurate label information.
However, it is not always possible to collect perfectly accurate labels for training samples …

The pari-mutuel model is a betting scheme that has its origins in horse racing, and that has
been applied in a number of contexts, mostly economics. In this paper, we consider the set …

Sum-product networks are a relatively new and increasingly popular family of probabilistic
graphical models that allow for marginal inference with polynomial effort. They have been …

Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted
topological structure, which are tailored to classifying data instances into multiple …

A credal classifier for multilabel data is presented. This is obtained as an extension of
Zaffalon's naive credal classifier to the case of non-exclusive class labels. The dependence …

Sensitivity analysis in probabilistic discrete graphical models is usually conducted by
varying one probability at a time and observing how this affects output probabilities of …

Abstract Human Reliability Analysis aims at identifying, quantifying and proposing solutions
to human factors causing hazardous consequences. Quantifying the influence of the human …