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About

The Stochastic Processes and Applied 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, there is

StochProc Software

Software

R packages rSPDE: Statistical methods for fractional SPDEs, with interfaces to R-INLA and inlabru. See the package homepage for more information. MetricGraph: Statistical analysis of data on metric graphs, such as street or river networks. See the package homepage for more information. excursions: Excursion sets and contour credible regions for latent Gaussian models. See the package homepage for more information. ngme2: Statistical modeling using latent non-Gaussian random fields. See the package homepage for more information. Implementations for papers R package and code to replicate the

Publications

Publications

Preprints available online 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) A new class of non-stationary Gaussian fields with general smoothness on metric

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Stochastic Processes and Applied Statistics Research Group

Professor David Bolin leads the so-called StochProc research group, which stands for Stochastic Processes and Applied Statistics Research Group ‌The StochProc 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.