Bayesian approaches to statistical inference combine prior beliefs with observed data through a likelihood function to yield a posterior distribution, offering a coherent framework for uncertainty ...

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 ...

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 ...

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

Main topics of the workshop are the application and development of Bayesian inference, AI/ML-approaches and the maximum entropy principle to inverse problems in science, machine learning, information ...

Industry groups and drugmakers want the US Food and Drug Administration (FDA) to explicitly clarify that Bayesian statistical methods can be used for products beyond those intended for children and ...