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 ...

It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue ...

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 ...

Most chatter about AI in other than research and academic institutions is about Machine Learning (ML) and various forms of neural nets and deep learning. Natural Language (speech recognition, language ...

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 ...

Mike Lee receives relevant research funding from the Australian Research Council, the Australia-Pacific Science Foundation, and Flinders University. Benedict King receives funding from the Australian ...