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 …

Abstract Probabilistic Answer Set Programming offers an intuitive and powerful declarative
language to represent uncertainty about combinatorial structures. Remarkably, under the …

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 …

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

Limited memory influence diagrams are graph-based models that describe decision
problems with limited information such as planning with teams and/or agents with imperfect …

Influence diagrams (ID) and limited memory influence diagrams (LIMID) are flexible tools to
represent discrete stochastic optimization problems, with the Markov decision process …

Influence diagrams are intuitive and concise representations of structured decision
problems. When the problem is non-Markovian, an optimal strategy can be exponentially …

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model
formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very …