Abstract Spaces with locally varying scale of measurement, like multidimensional structures
with differently scaled dimensions, are pretty common in statistics and machine learning …

There are a few different angles from which to examine the question of how to determine
whether social networks, which belong to the category of complex systems, are neither …

Bayesian network (BN) is an effective tool for causal inferences of accidents. However, it is
often criticized for the difficulty in obtaining accurate/sufficient data needed to get precise …

Influence diagrams are widely employed to represent multi-stage decision problems in
which each decision is a choice from a discrete set of alternative courses of action, uncertain …

We study the computational complexity of counterfactual reasoning in relation to the
complexity of associational and interventional reasoning on structural causal models …

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 networks extend Bayesian networks to allow for imprecision in probability values.
Marginal MAP is a widely applicable mixed inference task that identifies the most likely …

To perform the reliability analysis of complex and large-scale systems, Bayesian network
(BN) can be useful as it facilitates modelling the causal relationship between multiple types …

Belief functions as a powerful model to represent and deal with uncertain information are
widely used in information fusion. However, semantic association within a frame of …

Process systems are sensitive and vital industrial facilities. Disturbances in their
performance may cause harm to the environment and humans and/or significant economic …