The XVIth Journées de Géostatistique will take place on September 7 and 8, 2023, in Fontainebleau.

The event is co-organized by the Geostatistics team at the Mines Paris Geosciences Center and the RESSTE (Risques, Extremes et Statistique Spatio-TEmporelle) network, and sponsored by the GEOLEARNING chair.

Ahead of the conference, a one-day workshop to discover the gstlearn package, developed by researchers in the Mines Paris geostatistics team, will be held on September 6, 2023.

Registration (to the workshop and to the conference) is free but compulsory. As of August 25, new registrants will only be able to attend the conference remotely, and it will no longer be possible to register for the gstlearn workshop or for the conference dinner (scheduled on the evening of the 7th). Zoom links will be emailed to registered participants.

News: The programs of the conference and of the workshop are now online!

Guest speakers:

• François Bachoc (Institut de Mathématiques de Toulouse): Introduction to Gaussian process with inequality constraints, Application to coast flooding risk

• Gérard Biau (Sorbonne Université): Some statistical insights into physics-informed neural networks

• Ottmar Cronie (University of Gothenburg): Point process learning: A new statistical approach for point processes

• Anne Estrade (Université Paris Cité): Inférence statistique pour des champs gaussiens basée sur des caractéristiques géométriques
  

• Carlo Gaetan (Università Ca' Foscari): On modelling space-time threshold exceedances
  

• Bertrand Iooss (EDF R&D): Global sensitivity analysis of model outputs with dependent inputs: New insights around Shapley effects

• Alessandra Menafoglio (Politecnico di Milano): Object Oriented Spatial Statistics for the analysis of georeferenced complex data

Call for papers

    Abstract (400 words maximum, registration required) submission deadline: Friday, July 28, 2023

    Notification of accepted contributions: Friday August 4, 2023

The preferred themes of these Journées are:

• Machine learning approaches for spatial and spatio-temporal data
• Gaussian processes
• Large spatial and spatio-temporal datasets
• Spatio-temporal modeling
• Spatial extremes
• Point processes