About Research The Stochastic Processes and Mathematical Statistics group, led by Prof. David Bolin, develops methodology for statistical models involving stochastic processes and random fields. A main focus is the development of statistical methods based on stochastic partial differential equations. This is an exciting research topic that combines methods from statistics and applied mathematics in order to construct more flexible statistical models and better computational methods for statistical inference. Some current areas of focus are: Random fields on metric graphs In many statistical applications
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
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
Software Research Our Software The StochProc group develops open-source R packages for advanced statistical modeling of stochastic processes and spatial data. Each package reflects our commitment to creating accessible, high-performance tools for applied statistics and spatio-temporal analysis. rSPDE Statistical methods for fractional SPDEs, with interfaces to R-INLA and inlabru. MetricGraph Statistical analysis of data on metric graphs, such as street or river networks. excursions Excursion sets and contour credible regions for latent Gaussian models. ngme2 Statistical modeling using latent non-Gaussian random
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
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
Publications Publications Preprints available online D. Bolin, W. Li, D. Sanz-Alonso (2025) Bayesian Inverse Problems on Metric Graphs D. Saduakhas, D. Bolin, X. Jin, A.B. Simas, J. Wallin (2025) Incorporating Correlated Nugget Effects in Multivariate Spatial Models: An Application to Argo Ocean Data J. Jiang, J. Richards, R. Huser, D. Bolin (2025) Separation-based causal discovery for extremes D. Bolin, A.B. Simas (2025) rSPDE: tools for statistical modeling using fractional SPDEs D. Bolin, D. Saduakhas, A.B. Simas (2025) Log-Gaussian Cox Processes on General Metric Graphs D. Bolin, L. Riera-Segura, A.B. Simas (2025)
Stochastic Processes and Mathematical Statistics Front Page Led by Prof. David Bolin, the Stochastic Processes and Mathematical Statistics research group develops methodology for statistical models involving stochastic processes and random fields. A main focus is the development of statistical methods based on stochastic partial differential equations (SPDEs), and current main areas of research are random fields on metric graphs and networks, stochastic processes formulated through fractional-order SPDEs, and non-Gaussian random fields. More details on our work can be found in the About section, and details on the software we develop can be found in
