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We present the first fully variational Bayesian inference scheme forcontinuous Gaussian-process-modulated Poisson processes. Such point processesare used in a variety of domains, including neuroscience, geo-statistics andastronomy, but their use is hindered by the computational cost of existinginference schemes. Our scheme: requires no discretisation of the domain; scaleslinearly in the number of observed events; and is many orders of magnitudefaster than previous sampling based approaches. The resulting algorithm isshown to outperform standard methods on synthetic examples, coal miningdisaster data and in the prediction of Malaria incidences in Kenya.

Variational Inference for Gaussian Process Modulated Poisson Processes