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 …

Classical probability is not the only mathematical theory of uncertainty, or the most general.
Many authors have argued that probability theory is ill-equipped to model the 'epistemic' …

Safety and Reliability–Theory and Applications contains the contributions presented at the
27th European Safety and Reliability Conference (ESREL 2017, Portorož, Slovenia, June 18 …

The estimation of the conditional failure rate (CFR) of an overhead transmission line (OTL) is
essential for power system operational reliability assessment. It is hard to predict the CFR …

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

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 …

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 …

Bayesian Networks (BNs) are an extremely attractive tool in many fields of engineering and
science. Nevertheless, many studies have highlighted the limitations of the traditional BNs …

Probability intervals provide an intuitive, powerful and unifying setting for encoding and
reasoning with imprecise beliefs. This paper addresses the problem of updating uncertain …

A risk-informed decision making approach has been proposed in Mancuso et al.(2017) to
identify the cost-effective portfolios of safety barriers to be installed in a system for …