Bayesian inference is becoming an increasingly popular framework for statistics in the behavioral sciences. However, its application is hampered by its computational intractability - almost all ...

Abstract: Naïve Bayesian inference enables classification or prediction of an event given observations of potentially contradictory evidences, and is particularly intriguing in power-limited contexts ...

Abstract: Tracking inertia in real-time has emerged as a crucial topic for the modern low-inertia power system. However, in practice, large disturbances are infrequent, posing a challenge for routine ...

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 ...

A comprehensive JavaScript library for probabilistic modeling and statistical inference. This library provides production-ready implementations of Bayesian Networks, Hidden Markov Models, Gaussian ...

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 ...

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