latent Gaussian models

Dr. Haziq Jamil Delivers International Guest Lecture at UIN Mataram

1 min read · Tue, Apr 28 2026

R-INLA Item Response Theory Latent variable models latent Gaussian models

Our research group continues to expand its global footprint and strengthen international academic ties. Dr. Haziq Jamil, a Research Specialist in the BAYESCOMP team at KAUST, was invited to deliver an online international guest lecture at the Universitas Islam Negeri (UIN) Mataram in Indonesia.

A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework

Xiaotian Jin, Ph.D. Student, Statistics

Apr 29, 15:00 - 16:30

B4 R5209

latent Gaussian models spatio-temporal statistics stochastic algorithms multivariate statistics

This thesis develops a linear latent non-Gaussian modeling framework that extends latent Gaussian models to accommodate skewness, heavy tails, and extremes while preserving computational tractability, along with its implementation in the R package ngme2.

On Cross Validation, Log Gaussian Cox Process and Variational Bayes

Hans Montcho, Ph.D. Student, Statistics

Apr 29, 10:00 - 12:00

B2 R5220; Zoom Meeting 99219689653

Bayesian computational technique Bayesian computational statistics latent Gaussian models Point patterns spital point patterns

This dissertation advances Bayesian computation by developing a cross validation approach to assess LGCPs defined on Euclidean, manifolds or network domains and improving skewed posterior approximations for LGMs.

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

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.