Going off grid: Computationally efficient inference for log-Gaussian Cox processes

D. Simpson, J. B. Illian, F. Lindgren, S. H. Sørbye, H. Rue

Research output: Contribution to journalArticlepeer-review

81 Scopus citations


This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making use of a continuously specified Gaussian random field. We show that for sufficiently smooth Gaussian random field prior distributions, the approximation can converge with arbitrarily high order, whereas an approximation based on a counting process on a partition of the domain achieves only first-order convergence. The results improve upon the general theory of convergence for stochastic partial differential equation models introduced by Lindgren et al. (2011). The new method is demonstrated on a standard point pattern dataset, and two interesting extensions to the classical log-Gaussian Cox process framework are discussed. The first extension considers variable sampling effort throughout the observation window and implements the method of Chakraborty et al. (2011). The second extension constructs a log-Gaussian Cox process on the world's oceans. The analysis is performed using integrated nested Laplace approximation for fast approximate inference.

Original languageEnglish (US)
Pages (from-to)49-70
Number of pages22
Issue number1
StatePublished - Jan 1 2015


  • Approximation of Gaussian random fields
  • Gaussian Markov random field
  • Integrated nested Laplace approximation
  • Spatial point process
  • Stochastic partial differential equation

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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