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 ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

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 ...

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

The European Medicines Agency (EMA) has begun a consultation into the use of Bayesian methods in the analysis of clinical trial data. Bayesian methods are one of the main approaches to statistical ...

WASHINGTON, Jan. 20, 2026 /PRNewswire/ -- The U.S. Food and Drug Administration (FDA) has issued new draft guidance modernizing statistical methodologies used in clinical trials, formally recognizing ...

On Wednesday the 1st of April 2026, M.Eng. Chengkun Li defends his PhD thesis on Surrogate-based methods for efficient Bayesian posterior computation. The thesis is related to research done in the ...

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 ...