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 …

Continuous-time Markov chains are mathematical models that are used to describe the state-
evolution of dynamical systems under stochastic uncertainty, and have found widespread …

Background This study aimed to determine conditional dependence relationships of
variables that contribute to psychological vulnerability associated with suicide risk. A …

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 …

We study the limit behaviour of upper and lower bounds on expected time averages in
imprecise Markov chains; a generalised type of Markov chain where the local dynamics …

The results in this paper add useful tools to the theory of sets of desirable gambles, a
growing toolbox for reasoning with partial probability assessments. We investigate how to …

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 …

Statistical matching (also known as data fusion, data merging, or data integration) is the
umbrella term for a collection of methods which serve to combine different data sources. The …

Fault interpretation in geology inherently involves uncertainty, which has driven the need to
develop methods to quantify and analyze this uncertainty. This paper introduces a novel …