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: Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian ...

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

You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.

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 ...