Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

ABSTRACT. Stakeholder participation is becoming increasingly important in water resources management. In participatory processes, stakeholders contribute by putting forward their own perspective, and ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

How does one model a simple cell-signaling pathway? Consider a simple example consisting of a stimulant, an extracellular signal, an inhibitor of the signal, a G protein–coupled receptor, a G protein ...

A new technical paper titled “Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks” was published by researchers at Université Grenoble Alpes, CEA, ...

Bayesian Networks, also known as Belief Networks or Bayes Nets, are a powerful probabilistic graphical model used for reasoning under uncertainty. They have been successfully applied to a wide range ...

Background: As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain ...

On Friday the 11th of November 2022, PhD, M.Sc. Laura Uusitalo defends her PhD thesis on Bayesian network modelling of complex systems with sparse data: Ecological case studies. The thesis is related ...

Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his ...