Bayesian graphical models provide a principled framework for representing complex dependency structures among multivariate variables by combining graph theory with probabilistic inference. In these ...

In this work, we develop a new framework for designing experiments that are robust to model misspecification through generalised Bayesian inference. This repository contains the files needed to ...

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 ...

Department of Physics, Arizona State University, Tempe, Arizona 85281, United States Center for Biological Physics, Arizona State University, Tempe, Arizona 85281, United States College of Medical and ...

Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...

Stable distributions are well-known for their desirable properties and can effectively fit data with heavy tail. However, due to the lack of an explicit probability density function and finite second ...

Abstract: A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

pygeoinf is a Python library for solving geophysical inference and inverse problems in a coordinate-free, abstract framework. It leverages the mathematics of Hilbert ...