Going off grid: computationally efficient inference for log-Gaussian Cox processes
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- Avishek Chakraborty & Alan E. Gelfand & Adam M. Wilson & Andrew M. Latimer & John A. Silander, 2011. "Point pattern modelling for degraded presence‐only data over large regions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(5), pages 757-776, November.
- Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
- Waagepetersen, Rasmus, 2004. "Convergence of posteriors for discretized log Gaussian Cox processes," Statistics & Probability Letters, Elsevier, vol. 66(3), pages 229-235, February.
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- David L. Miller & Richard Glennie & Andrew E. Seaton, 2020. "Understanding the Stochastic Partial Differential Equation Approach to Smoothing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 1-16, March.
- Janine B. Illian & David F. R. P. Burslem, 2017. "Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 495-520, October.
- Unn Dahlén & Johan Lindström & Marko Scholze, 2020. "Spatiotemporal reconstructions of global CO2‐fluxes using Gaussian Markov random fields," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
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