Bayesian inference provides a robust framework for combining prior knowledge with new evidence to update beliefs about uncertain quantities. In the context of statistical inverse problems, this ...

A collaboration including the University of Oxford, University of British Columbia, Intel, New York University, CERN, and the National Energy Research Scientific Computing Center is working to make it ...

In a new draft guidance issued on January 14, 2026, the FDA discussed the use of a modern statistical methodology in clinical trials designed to ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about ...

In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, ...

Approach developed at the Texas A&M School of Public Health offers promising new knowledge on idiopathic pulmonary fibrosis pathways Texas A&M University A new statistical technique developed by a ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...