Faults in the process industry can be diagnosed using various data-driven methods, but the
intrinsic relationships between inputs and outputs, particularly the physical consistency of …

For efficient process control and monitoring, accurate real-time information of quality
variables is essential. To predict these quality (or slow-rate) variables at a fast-rate, in the …

Identification of Parkinson's disease subtypes may help understand underlying disease
mechanisms and provide personalized management. Although clustering methods have …

Bayesian networks are a popular class of multivariate probabilistic models as they allow for
the translation of prior beliefs about conditional dependencies between variables to be …

We describe a general approach for maximum a posteriori (MAP) inference in a class of
discrete-continuous factor graphs commonly encountered in robotics applications. While …

Causality has been a burgeoning field of research leading to the point where the literature
abounds with different components addressing distinct parts of causality. For researchers, it …

This tutorial presents the factor graph, a recently introduced estimation framework that is a
generalization of the Kalman filter. An approach for constructing a factor graph, with its …

Designing security mechanisms for cloud computing infrastructures has assumed
importance with the widespread adoption of public clouds. Virtualization security is a crucial …

Virtualization security is an important aspect to be carefully addressed while provisioning
cloud services. In this paper, we propose a novel model using Bayesian Attack Graphs …

In the nuclear industry, Severe Accident Management Guidelines (SAMGs) are developed
with the objective of providing prescriptive actions for mitigating the consequences of …