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

@article{Simpson2011GoingOG, title={Going off grid: computationally efficient inference for log-Gaussian Cox processes}, author={Daniel P. Simpson and Janine B. Illian and Finn Lindgren and Sigrunn H. S{\o}rbye and H. Rue}, journal={Biometrika}, year={2011}, volume={103}, pages={49-70} }

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…

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