applied statistics

excursions

Research Resources

latent Gaussian models spatial statistics applied statistics bayesian inference R-INLA Gaussian random fields excursion sets

excursions: A Framework for Probabilistic Excursion Sets and Contour Inference

rSPDE

Research Resources

spatial statistics applied statistics Gaussian random fields SPDEs bayesian inference geostatistics

rSPDE: A Computational Framework for Rational Approximations of Fractional Stochastic Partial Differential Equations

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

A new class of random field models for data on networks

David Bolin, Professor, Statistics

Oct 3, 12:00 - 13:00

B9 L2 R2322 H1

applied statistics machine learning Gaussian processes

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.