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 …

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are
growing in popularity. Their versatility and modelling power is now employed across a …

Survival prediction and treatment selection in lung cancer care are characterised by high
levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain …

This study aimed at modelling the underlying predictor of ASCVD through the Bayesian
network (BN). Data for the AZAR Cohort Study, which evaluated 500 healthcare providers in …

Interstate pipelines are the most efficient and feasible mean of transport for crude oil and gas
within boarders. Assessing the risks of these pipelines is challenging despite the evolution …

The correct diagnosis of cardiovascular disease is a key factor to reduce social and
economic costs. In this context, cardiovascular disease risk assessment tools are of …

Bayesian networks (BNs) are rapidly becoming a leading tool for applied Artificial
Intelligence. Although BNs have been used successfully for many medical diagnosis …

Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to
increase as the European population ages. In this context, clinical guidelines recommend …

Oil and gas industries apply several techniques for assessing and mitigating the risks that
are inherent in its operations. In this context, the application of Bayesian Networks (BNs) to …

The common interpretation of Bayesian networks is that they are vehicles for representing
probability distributions, in a graphical form supportive of human understanding and with …