Modeling complex spatiotemporal dependencies in correlated traffic series is essential for
traffic prediction. While recent works have shown improved prediction performance by using …

Data analytics plays a vital role in Industry 4.0, guiding decisions and operations in key
areas including process monitoring, reliability assessment and soft sensing. Bayesian …

Bayesian networks are probabilistic graphical models that are commonly used to represent
the uncertainty in data. The PyBNesian package provides an implementation for many …

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs)
from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic …

Learning the structure of continuous-time Bayesian networks directly from data has
traditionally been performed using score-based structure learning algorithms. Only recently …

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 …

Off-policy learning in dynamic decision problems is essential for providing strong evidence
that a new policy is better than the one in use. But how can we prove superiority without …

We present the application of a cyber-physical system for inprocess quality control based on
the visual inspection of a laser surface heat treatment process. To do this, we propose a …

Um Maschinelles Lernen (ML) in sicherheitskritischen oder autonomen Systemen
einzusetzen sind G̈utegarantien und Fehlerschranken erforderlich—eine rein empirische …

With the rapid development of artificial intelligence and data science, Dynamic Bayesian
Network (DBN), as an effective probabilistic graphical model, has been widely used in many …