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 school is centered around two main themes:

• Non-parametric Bayesian statistics, which includes methods such as Gaussian Processes and Dirichlet Process Mixtures, offering flexible tools for modeling complex, high-dimensional data without assuming a fixed parametric form.
• Stochastic Partial Differential Equations (SPDEs) for spatial statistics, providing a principled framework for modeling spatial and spatiotemporal processes with inherent randomness and complex dependencies.

The event will focus on both theoretical advancements and practical applications of these methods, featuring discussions on the latest developments in non-parametric Bayesian approaches, scalable inference algorithms, and innovations in SPDEs including efficient numerical methods and applications to real-world spatial data.

Speakers

Master classes, tutorials and invited talks (To be announced)

Invited speakers and course instructors will be announced in due course.

The scientific program will feature:

• 2 Master classes (2 x 2h each)
• 2 Tutorials (2h each)
• Invited talks (1h each)
• Contributed talks & poster presentations

Program

Overview & Timetable (To be announced)

The detailed schedule and list of talks will be published closer to the event.

Program Overview

The week is organized around multiple content formats covering foundational theory, scalable computational tools, and domain applications:

• Master classes & Foundational Lectures: In-depth multi-hour courses suitable for PhD students, postdocs, and researchers.
• Tutorials & Hands-on Practicals: Interactive sessions demonstrating scalable Bayesian computation and software implementations.
• Invited Plenary Talks & Contributed Talks: Selected presentations from international researchers on cutting-edge methodological advances.
• Poster Sessions: An interactive forum to present ongoing work and foster collaborations across disciplines.
• Panel Discussions & Working Sessions: Discussions on open challenges, industry/academic funding, and informal meetings.
• Social Events: Informal discussions, conference dinner, and the traditional hike in the Parc National des Calanques.

Information

Dates and Venue The autumn school will take place in Autumn 2027 at CIRM (Centre International de Rencontres Mathématiques), Luminy, Marseille, France.

Registration & Practical Details Registration dates, travel guidelines, and practical information regarding accommodation at CIRM will be announced in due course.

Sponsors & Partners

• Centre International de Rencontres Mathématiques

Committees

• Organizing Committee
  • Sophie Ancelet
  • Julyan Arbel
  • Romain Azaïs
  • Mitra Fouladirad
  • Guillaume Kon Kam King
  • Thomas Opitz
  • Angelina Roche

• Scientific Committee
  • (To be announced)