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dc.contributor.authorSimpson, Daniel
dc.contributor.authorIllian, Janine Baerbel
dc.contributor.authorLindgren, Finn
dc.contributor.authorSørbye , Sigrunn H.
dc.contributor.authorRue, Haavard
dc.identifier.citationSimpson , D , Illian , J B , Lindgren , F , Sørbye , S H & Rue , H 2016 , ' Going off grid : computationally efficient inference for log-Gaussian Cox processes ' , Biometrika , vol. 103 , no. 1 , pp. 49-70 .
dc.identifier.otherPURE: 9507074
dc.identifier.otherPURE UUID: 29ebc41d-4a3e-486a-a869-dc3a03a6ad5e
dc.identifier.otherScopus: 84960106685
dc.identifier.otherWOS: 000371685300004
dc.description.abstractThis 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.
dc.rights© 2016 Biometrika Trust. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectApproximation of Gaussian random fieldsen
dc.subjectGaussian Markov random fielden
dc.subjectIntegrated nested Laplace approximationen
dc.subjectSpatial point processen
dc.subjectStochastic partial differential equationen
dc.subjectGC Oceanographyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleGoing off grid : computationally efficient inference for log-Gaussian Cox processesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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