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 …

Bayesian networks are a type of probabilistic graphical models lie at the intersection
between statistics and machine learning. They have been shown to be powerful tools to …

The wide popularity of Android systems has been accompanied by increase in the number
of malware targeting these systems. This is largely due to the open nature of the Android …

This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …

In this thesis I address the important problem of the determination of the structure of directed
statistical models, with the widely used class of Bayesian network models as a concrete …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

We present a new approach to structure learning in the field of Bayesian networks. We
tackle the problem of the search for the best Bayesian network structure, given a database of …

We propose a new scoring function for learning Bayesian networks from data using score+
search algorithms. This is based on the concept of mutual information and exploits some …

Physiological mechanisms in human biology, the progress of disease in individual patients,
hospital work-flow management: these are just a few of the many complicated processes …