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 exciting line of research combines methods from statistics and applied mathematics in order to construct flexible and physically interpretable statistical models, and efficient computational methods for statistical inference. Current areas of focus include the following: Random fields on metric graphs Many
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
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
