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

Approximate Bayesian Computation (ABC) is a likelihood‐free inference methodology that has revolutionised the way researchers tackle complex problems where the likelihood function is difficult or ...

Researchers have published an article arguing that Bayesian methodology, a statistical tool introduced by Rev. Thomas Bayes in the 18th Century, is vital in providing solutions to many difficult ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

Extended educational sessions that offer attendees the opportunity to learn research methods and techniques from prominent psychological scientists.

Concr CEO Irina Babina and CTO Matthew Griffiths unpack how Bayesian foundation models can excel at uncertainty management to ...

Offered through an interdisciplinary partnership, data science at CU Boulder is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science and awarded by the ...

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal ...