Notícies del la SocE

Article convidat de la revista SORT 2024: A diffusion-based spatio-temporal extension of Gaussian Matérn fields

– La revista SORT (Statistics and Operations Research Transactions) es complau a convidar-vos a la presentació de l’article  “A diffusion-based spatio-temporal extension of Gaussian Matérn fields”. Autors: F. Lindgren, H. Bakka, D. Bolin, E. Krainski and H. Rue.

La presentació serà a càrrec dels professors Finn Lindgren i Haavard Rue.

Resum: Gaussian random fields with Matérn covariance functions are popular models in spatial statistics and machine learning. In this work, we develop a spatio-temporal extension of the Gaussian Matérn fields formulated as solutions to a stochastic partial differential equation. The spatially stationary subset of the models have marginal spatial Matérn covariances, and the model also extends to Whittle-Matérn fields on curved manifolds, and to more general non-stationary fields. In addition to the parameters of the spatial dependence (variance, smoothness, and practical correlation range) it additionally has parameters controlling the practical correlation range in time, the smoothness in time, and the type of non-separability of the spatio-temporal covariance. Through the separability parameter, the model also allows for separable covariance functions. We provide a sparse representation based on a finite element approximation, that is well suited for statistical inference and which is implemented in the R-INLA software. The flexibility of the model is illustrated in an application to spatio-temporal modeling of global temperature data.


Finn Lindgren is a Professor of Statistics in the School of Mathematics at the University of Edinburgh. He received a PhD in Engineering in Mathematical Statistics at Lund University (2003), and has since worked as lecturer and research fellow at Lund University and Norwegian Institute of Science and Technology in Trondheim, followed by four years as Reader at the University of Bath, before joining the growing Statistics group in Edinburgh in 2016. He has served as Associate Editor of Annals of Applied Statistics, as member of the Royal Statistical Society Research Committee, and is an Elected Member of ISI. His research covers spatial and spatio-temporal modelling and computational Bayesian methods. In particular, the development of stochastic partial differential equations to enable the use of computationally efficient methods for sparse matrices and Markov random fields lead to an RSS read paper in 2011. The subsequent software development, including the MCMC-free Bayesian statistics R packages INLA, inlabru, and excursions, have lead to involvement in a broad range of applications, including large scale modelling for climate science, point process models for animal abundance in ecology and earthquake forecasting in geoscience, as well as animal movement models, finance, genetics, and epidemiology.

Haavard Rue is a professor of Statistics at CEMSE Division, at the King Abdullah University of Science and Technology in Saudi Arabia, since 2017, and before that a professor at the Department of Mathematical Sciences at the Norwegian University for Science and Technology. He was named a highly cited researcher according to the Highly Cited Researchers in the years 2019–2021, from the Web of Science Group. He gave the Bahadur Memorial Lectures at Univ of Chicago in 2018, and was in 2021 awarded the Royal Statistical Society (RSS) Guy Medal in Silver for his work on Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approach represent and compute with Gaussian fields. His research is mainly centred around the “R-INLA project”, see

Aquest acte es durà a terme el dilluns 15 d’abril, a les 12h a 13h, a l’Institut d’Estudis Catalans la sala Nicolau d’Olwer.

Us hi esperem!