Abstract Prognostic and systems Health Management (PHM) is an integral part of a system.
It is used for solving reliability problems that often manifest due to complexities in design …

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 …

A structural causal model is made of endogenous (manifest) and exogenous (latent)
variables. We show that endogenous observations induce linear constraints on the …

We assume to be given structural equations over discrete variables inducing a directed
acyclic graph, namely, a structural causal model, together with data about its internal nodes …

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets
of distributions. An algorithm for approximate credal network updating is presented. The …

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 …

An issue that has so far received only limited attention in probabilistic logic programming
(PLP) is the modelling of so-called epistemic uncertainty, the uncertainty about the model …

An imprecise Bayesian nonparametric approach to system reliability with multiple types of
components is developed. This allows modelling partial or imperfect prior knowledge on …

Probabilistic circuits are a class of probabilistic generative models that allow us to compute
different types of probabilistic queries in polynomial time. Unlike many of the mainstream …

Bayesian networks learning is computationally expensive even in the case of sacrificing the
optimality of the result. Many methods aim at obtaining quality solutions in affordable times …