To be acceptably safe one must identify the risks one is exposed to and decide what risk
reducing measures are required. It is uncertain whether the threat really will materialize, but …

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 …

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 …

Securing critical infrastructures is a complex task. Required information is usually scarce or
inexistent, and experts' judgments may be inaccurate and biased. In this paper, two …

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

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 …

Credal networks are graph-based statistical models whose parameters take values in a set,
instead of being sharply specified as in traditional statistical models (eg, Bayesian …

Background Most terminally ill cancer patients prefer to die at home, but a majority die in
institutional settings. Research questions about this discrepancy have not been fully …

When using convex probability sets (or, equivalently, lower previsions) as uncertainty
models, identifying extreme points can help simplifying various computations or the use of …

The authors of this article have developed six probabilistic causal models for critical risks in
tunnel works. The details of the models' development and evaluation were reported in two …