Log-Gaussian Cox process

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

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

1 min read · Mon, Oct 18 2021

landslide INLA Log-Gaussian Cox process

New paper accepted: Opitz, T., Bakka, H., Huser, R. , and Lombardo, L. (2021+), High-resolution Bayesian mapping of landslide hazard with unobserved trigger event , Annals of Applied Statistics, to appear [ arXiv][ PDF preprint ].