latent Gaussian models

MetricGraph

Research Resources

spatial statistics applied statistics bayesian analysis Bayesian and computational Statistics Bayesian Statistics Log-Gaussian Cox process latent Gaussian models Gaussian processes INLA MetricGraph Metric graphs

MetricGraph: A Statistical Framework for Modeling Gaussian Fields on Metric Graphs

Mastering Network Spatial Statistics with the MetricGraph R Package

1 min read · Sat, May 24 2025

spatial statistics applied statistics Bayesian Statistics Log-Gaussian Cox process Gaussian processes latent Gaussian models

A new open-access mini-course is expanding access to advanced network spatial statistics through practical training with the MetricGraph package R, a powerful tool developed by David Bolin (KAUST), Alexandre B. Simas (KAUST), and Jonas Wallin (Lund University) for modeling data on network structures such as road systems and river basins. Presented by Bolin and Simas at the University of Glasgow during the “ INLA: Past, Present, and Future” workshop, the mini-course provides step-by-step tutorials, real datasets, and comprehensive R code, all freely available online, opening new avenues for

Criticism and robustification of latent Gaussian models

Rafael Medeiros Cabral, Ph.D. Student, Statistics

May 28, 15:00 - 16:00

B1 L4 R4102

latent Gaussian models

Latent Gaussian models (LGM) are widely used but struggle with certain datasets that contain non-Gaussian features, such as sudden jumps or spikes. This dissertation aims to provide tools for researchers to check the adequacy of the fitted LGM (criticism); if the check fails, offer efficient and user-friendly implementations of latent non-Gaussian models, which lead to more robust inferences (robustification).

Haavard Rue

Program Chair, Statistics

Bayesian computational statistics bayesian methodology latent Gaussian models spatial statistics

Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.