Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning ...

In TNO, we use Bayesian Belief Networks (BBNs) for hypothesis testing. However, data to learn the structure of a BBN is often limited or missing in military contexts, which significantly reduces the ...

Understanding is often defined as the ability to form mental models of the world, reason about cause and effect, and predict ...

Network meta-analysis (NMA) is an increasingly popular statistical method of synthesising evidence to assess the comparative benefits and harms of multiple treatments in a single analysis. Several ...

The seven decades of "artificial intelligence" have been marked by exaggerated promises, surprising developments and ...

The new Verizon AI Connect comprises a network infrastructure and suite of products designed to enable global enterprises to deploy AI workloads at scale. It brings together and “reimagines ...

AI is becoming a game changer in network security, but it’s not always easy to decide which type of AI to use — generative, predictive or a mix of both. In the fast-evolving landscape of cyberthreats, ...

Criminal networks are vast and adaptable, making traditional law enforcement methods insufficient. AI has the power to change this dynamic by mapping criminal affiliations, tracing illicit ...

AI tools are increasingly being used to track and monitor us both online and in person, yet their effectiveness comes with significant risks. Computer scientists at the Oxford Internet Institute, ...

Arista Networks should benefit from increased AI capex by top clients Microsoft and Meta Platforms, signaling potential revenue growth beyond current market expectations. Most recently ...