Careful prior specification avoids incautious inference for log-Gaussian Cox point processes

Sigrunn H. Sørbye (Corresponding Author), Janine B. Illian, Daniel P. Simpson, David F. R. P. Burslem, Håvard Rue

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13 Citations (Scopus)
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Hyperprior specifications for random fields in spatial point process modelling
can have a major impact on the results. In fitting log-Gaussian Cox processes to rainforest tree species, we consider a reparameterised model combining a spatially structured and an unstructured random field into a single component. This component has one hyperparameter accounting for marginal variance, while an additional hyperparameter governs the fraction of the variance explained by the spatially structured effect. This facilitates interpretation of the hyperparameters and significance of covariates is studied for a range of hyperprior specifications. Appropriate scaling makes the analysis invariant to grid resolution.
Original languageEnglish
Pages (from-to)543-564
Number of pages20
JournalJournal of the royal statistical society series c-Applied statistics
Issue number3
Early online date2 Nov 2018
Publication statusPublished - Apr 2019

Bibliographical note

The BCI forest dynamics research project was founded by S.P. Hubbell and R.B. Foster and is now managed by R. Condit, S. Lao, and R. Perez under the Center for Tropical Forest Science and the Smithsonian Tropical Research in Panama. Numerous organizations have provided funding, principally the U.S. National Science Foundation, and hundreds of field workers have contributed. The data used can be requested and generally granted at Kriged estimates for concentration of the soil nutrients were downloaded from We acknowledge the principal investigators that were responsible for collecting and analysing the soil maps (Jim Dallin, Robert John, Kyle Harms, Robert Stallard and Joe Yavitt), the funding sources (NSF DEB021104,021115, 0212284,0212818 and OISE 0314581, STRI Soils Initiative and CTFS) and field assistants (Paolo Segre and Juan Di Trani).


  • Bayesian analysis
  • spatial point process
  • penalised complexity prior
  • R-INLA
  • Spatial modelling
  • Penalized complexity prior
  • Spatial point process


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