About Research 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
Software Research R packages MetricGraph: Statistical analysis of data on metric graphs, such as street or river networks. Available on CRAN. See the package homepage for more information. rSPDE: Implements rational approximation of fractional SPDEs, available on CRAN. See the package homepage for more information. excursions: Implements Excursion sets and contour credible regions for latent Gaussian models, available on CRAN. See the development page for more information. ngme: Methods for estimation and prediction using non-Gaussian random fields and longitudinal models. Implementations for papers R package
