Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

In the 20th-century statistics wars, Bayesians were underdogs. Now their methods may help speed treatments to market.

Google Research has 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 focuses ...

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...

We discuss the development of a course in Bayesian statistics that began as an offering to statistics graduate students, evolved into a course for graduate students in other departments, then was ...

With so many worlds out there, the question is: How many are home to advanced life? In a paper just published in the Proceedings of the National Academy of Sciences, David Kipping from Columbia ...

The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating ...