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 ...

Empirical Bayesian methods occupy a unique position at the interface of frequentist and Bayesian paradigms by estimating prior distributions directly from observed data. This approach preserves the ...

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 ...

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 ...