Going off grid: computationally efficient inference for log-Gaussian Cox processes
Abstract
This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making novel 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, while an approximation based on a counting process on a partition of the domain only achieves...
Paper Details
Title
Going off grid: computationally efficient inference for log-Gaussian Cox processes
Published Date
Mar 1, 2016
Journal
Volume
103
Issue
1
Pages
49 - 70
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