This paper presents a summary of Peter Walley's theory of coherent lower previsions. We
introduce three representations of coherent assessments: coherent lower and upper …

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 …

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 …

We give an overview of two approaches to probability theory where lower and upper
probabilities, rather than probabilities, are used: Walley's behavioural theory of imprecise …

A novel technique to classify time series with imprecise hidden Markov models is presented.
The learning of these models is achieved by coupling the EM algorithm with the imprecise …

Bayesian networks are powerful probabilistic graphical models for modelling uncertainty.
Among others, classification represents an important application: some of the most used …

We focus on credal nets, which are graphical models that generalise Bayesian nets to
imprecise probability. We replace the notion of strong independence commonly used in …

In several applications there is the need to consider different data sources and to integrate
information: a specific case is the so-called statistical matching, where data sources have …

We present a new approach for constructing regression and classification models for interval-
valued data. The risk functional is considered under a set of probability distributions …

The traditional way to cope with missing data problems has been to combine the available
data with assumptions strong enough to point-identify the probability distribution describing …