The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social ...

The Virtual Brain Inference (VBI) toolkit enables efficient, accurate, and scalable Bayesian inference over whole-brain network models, improving parameter estimation, uncertainty quantification, and ...

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 ...

A security analytics approach that exploits the unique strengths of Bayesian networks, machine learning and rules-based systems—while also compensating for or eliminating their individual ...

Bayesian networks, machine learning and rules-based systems individually don't work well. They don’t produce good results, don’t scale or are too hard to work with. Digital technologies have changed ...