Files in this item
Going off grid : computationally efficient inference for log-Gaussian Cox processes
Item metadata
dc.contributor.author | Simpson, Daniel | |
dc.contributor.author | Illian, Janine Baerbel | |
dc.contributor.author | Lindgren, Finn | |
dc.contributor.author | Sørbye , Sigrunn H. | |
dc.contributor.author | Rue, Haavard | |
dc.date.accessioned | 2017-02-06T00:31:48Z | |
dc.date.available | 2017-02-06T00:31:48Z | |
dc.date.issued | 2016-03 | |
dc.identifier | 9507074 | |
dc.identifier | 29ebc41d-4a3e-486a-a869-dc3a03a6ad5e | |
dc.identifier | 84960106685 | |
dc.identifier | 000371685300004 | |
dc.identifier.citation | Simpson , 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 . https://doi.org/10.1093/biomet/asv064 | en |
dc.identifier.issn | 0006-3444 | |
dc.identifier.uri | https://hdl.handle.net/10023/10232 | |
dc.description.abstract | 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. | |
dc.format.extent | 22 | |
dc.format.extent | 1986818 | |
dc.language.iso | eng | |
dc.relation.ispartof | Biometrika | en |
dc.subject | Approximation of Gaussian random fields | en |
dc.subject | Gaussian Markov random field | en |
dc.subject | Integrated nested Laplace approximation | en |
dc.subject | Spatial point process | en |
dc.subject | Stochastic partial differential equation | en |
dc.subject | GC Oceanography | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | 3rd-NDAS | en |
dc.subject | BDC | en |
dc.subject | R2C | en |
dc.subject.lcc | GC | en |
dc.subject.lcc | QA75 | en |
dc.title | Going off grid : computationally efficient inference for log-Gaussian Cox processes | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. School of Mathematics and Statistics | en |
dc.contributor.institution | University of St Andrews. Scottish Oceans Institute | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | 10.1093/biomet/asv064 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2017-02-05 | |
dc.identifier.url | http://www.math.ntnu.no/~daniesi/S10-2011.pdf | en |
This item appears in the following Collection(s)
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.