A dynamic Bayesian network (DBN) based fault detection, root cause diagnosis, and fault
propagation pathway identification scheme is proposed. The proposed methodology …

This paper presents a hybrid methodology to detect and diagnose the faults in dynamic
processes based on principal component analysis (PCA) with T 2 statistics and a Bayesian …

Structured data, such as sensor data, can provide valuable insights to safety practitioners for
develo** prevention and mitigation strategies. However, relying on a single data source …

Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in
environmental risk assessment (ERA) may have severe consequences for decision making …

Even though various frameworks exist for reasoning under uncertainty, a realistic fault
diagnosis task does not fit into any of them in a straightforward way. For each framework …

Data-driven methods are implemented using particularly complex scenarios that reflect in-
depth perennial knowledge and research. Hence, the available intelligent algorithms are …

This chapter offers an accessible introduction to the channel-based approach to Bayesian
probability theory. This framework rests on algebraic and logical foundations, inspired by the …

Modeling and predicting social interactions within shared physical spaces is key to
understanding human spatio-temporal behavior. Such models are essential for optimizing …

Medical decisions, especially when diagnosing Hepatitis C, are challenging to make as they
often have to be based on uncertain and fuzzy information. In most cases, that puts doctors …

Analyzing the effectiveness of digital marketing campaigns can be challenging due to the
large number of customer interactions across various online channels. Attribution modeling …