This Primer examines Skilling’s nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarize ...

Bayesian inference in nonparametric settings offers a coherent framework for learning complex, infinite-dimensional objects, such as probability densities, regression functions or solutions to inverse ...

This course provides a rigorous yet practical introduction to Bayesian methods for statistical inference, probabilistic modeling, knowledge representation, and decision-making under uncertainty.

On Friday the 18th of September 2026, M.Sc. Bernardo Williams defends his PhD thesis on Geometric Methods for Bayesian Inference and Modeling Discrete Data. The thesis is related to research done in ...

Main topics of the workshop are the application and development of Bayesian inference, AI/ML-approaches and the maximum entropy principle to inverse problems in science, machine learning, information ...

Approaches for statistical inference -- The Bayes approach -- Bayesian computation -- Model criticism and selection -- The empirical Bayes approach -- Bayesian design -- Special methods and models -- ...

Google Researchers have proposed a training method that teaches large language models to approximate Bayesian reasoning by learning from the predictions of an optimal Bayesian system. The approach ...

The US Food and Drug Administration (FDA) issued a draft guidance on Friday to assist sponsors in using Bayesian methods to support the safety and effectiveness of new drugs in clinical trials. These ...

Industry groups and drugmakers want the US Food and Drug Administration (FDA) to explicitly clarify that Bayesian statistical methods can be used for products beyond those intended for children and ...

How can the component elements of an unknown material, such as a meteorite, be determined? X-ray fluorescence analysis can be used to identify an abundance of elements, by irradiating samples with ...