Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

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 …

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 …

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 …

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 …

Two important tasks in probabilistic reasoning are the computation of the maximum posterior
probability of a given subset of the variables in a Bayesian network (MAP), and the …

Compatibility is the problem of checking whether some given probabilistic assessments
have a common joint probabilistic model. When the assessments are unconditional, the …

An algorithm for approximate credal network updating is presented. The problem in its
general formulation is a multilinear optimization task, which can be linearized by an …

Abstract We present CREMA (Credal Models Algorithms), a Java library for inference in
credal networks. These models are analogous to Bayesian networks, but their local …

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