Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

Temporal data representing chronological observations of complex systems can be
ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Using domain/expert knowledge when learning Bayesian networks from data has been
considered a promising idea since the very beginning of the field. However, in most of the …

Despite increasing digitalization and automation, complex production processes often
require human judgment/decision-making adaptability. Humans can abstract and transfer …

Learning a Bayesian network is a difficult and well known task that has been largely
investigated. To reduce the number of candidate graphs to test, some authors proposed to …

Discovering causal relations in a knowledge base represents nowadays a challenging
issue, as it gives a brand new way of understanding complex domains. In this paper, we …

Probabilistic Graphical Models (PGMs) are powerful tools for representing and reasoning
under uncertainty. Although useful in several domains, PGMs suffer from their building …

Being able to provide explanations about a domain is a hard task that requires from a
probabilistic reasoning's viewpoint a causal knowledge about the domain variables …

Within the last years, probabilistic causality has become a very active research topic in
artificial intelligence and statistics communities. Due to its high impact in various …