Publications (42)
Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
David Bolin, Finn Lindgren
A new class of stochastic field models is constructed using nested stochastic partial differential equations (SPDEs). The model class is computationally efficient, applicable to da…
Fast approximate inference with INLA: the past, the present and the future
Daniel Simpson, Finn Lindgren, HÃ¥vard Rue
Latent Gaussian models are an extremely popular, flexible class of models. Bayesian inference for these models is, however, tricky and time consuming. Recently, Rue, Martino and Ch…
Spatially continuous modelling of aggregated outcome data
Stephen Jun Villejo, Peter Diggle, Finn Lindgren +5
This work develops a block aggregation approach to spatial estimation and prediction when the response is observed at a coarse spatial scale, for example as counts of events in adm…
Validating uncertainty propagation approaches for two-stage Bayesian spatial models using simulation-based calibration
Stephen Jun Villejo, Sara Martino, Janine Illian +2
This work tackles the problem of uncertainty propagation in two-stage Bayesian models, with a focus on spatial applications. A two-stage modeling framework has the advantage of bei…
Calculating probabilistic excursion sets and related quantities using excursions
David Bolin, Finn Lindgren
The R software package excursions contains methods for calculating probabilistic excursion sets, contour credible regions, and simultaneous confidence bands for latent Gaussian sto…
Joint Modelling of Line and Point Data on Metric Graphs
Karina Lilleborge, Sara Martino, Geir-Arne Fuglstad +2
Metric graphs are useful tools for describing spatial domains like road and river networks, where spatial dependence act along the network. We take advantage of recent developments…