92 citations · 232 across the 37 of their papers we have counts for
4 papers · 1 filter
Going off grid: Computationally efficient inference for log-Gaussian Cox processes
Daniel Simpson, Janine Illian, Finn Lindgren +2
This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making novel use of a continuous…
How do Markov approximations compare with other methods for large spatial data sets?
David Bolin, Finn Lindgren
The Matérn covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matérn covariance models are, however, often computationally infe…
Fast approximate inference with INLA: the past, the present and the future
Daniel Simpson, Finn Lindgren, Håvard 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…
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…