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