Background: Analysis of the human connectome using functional magnetic resonance
imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to …

Bayesian Networks are probabilistic graphical models that represent conditional
independence relationships among variables as a directed acyclic graph (DAG), where …

We propose to meta-learn causal structures based on how fast a learner adapts to new
distributions arising from sparse distributional changes, eg due to interventions, actions of …

Data in the biological, chemical, and clinical domains are accumulating at ever‐increasing
rates and have the potential to accelerate and inform drug development in new ways …

The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

• A new chapter on Partially Observable Markov Decision Process has been incorporated,
which includes a detailed introduction to these models, approximate solution techniques …

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic
safety context are becoming more popular. This paper discusses the utilization of Bayesian …

This paper presents Fraud-BNC, a customized Bayesian Network Classifier (BNC) algorithm
for a real credit card fraud detection problem. The task of creating Fraud-BNC was …

The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic
system by Markov trees can achieve the minimum information loss with the topology of a …