Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

Supervised Machine Learning (SML) is the search for algorithms that reason from externally
supplied instances to produce general hypotheses, which then make predictions about …

The social participation and integration of older adults are important aspects of healthy
aging. However, in general, older adults have smaller social networks than their younger …

Process Industries handling, producing and storing bulk amount of hazardous materials are
a major source of concern in terms of both safety and security. Safety and security cannot be …

The proliferation of technologies embedded in connected and autonomous vehicles (CAVs)
increases the potential of cyber-attacks. The communication systems between vehicles and …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

The goal of supervised learning is to build a concise model of the distribution of class labels
in terms of predictor features. The resulting classifier is then used to assign class labels to …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

Probabilistic graphical models and decision graphs are powerful modeling tools for
reasoning and decision making under uncertainty. As modeling languages they allow a …