Save the date, the next Bayes@CIRM will be in Autumn 2027!
The development and analysis of robust Bayesian methods for high-dimensional statistical settings are fundamental in addressing modern real-world learning problems. These approaches are particularly attractive due to their inherent capability to quantify the uncertainty associated with any statistical estimation procedure by computing or approximating posterior distributions.
The scientific core of this week-long autumn school is centered around two main themes: non-parametric Bayesian statistics and stochastic partial differential equations (SPDEs) for spatial statistics. The event will highlight all aspects of such approaches, from theoretical advancements and scalable algorithms to applications in environmental and climate science.
The scientific program usually includes mini courses, plenary talks, contributed talks and practical sessions.