Sparse Approximate Inference for Spatio-Temporal Point Process Models

Botond Cseke, Guido Sanguinetti, Andrew Zammit Mangion

Research output: Contribution to journalArticlepeer-review


Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretised log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both non-linear filtering-smoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with both Gaussian and point-process observations and use it to reconstruct the conflict intensity and dynamics in Afghanistan from the WikiLeaks Afghan War Diary.
Original languageEnglish
Pages (from-to)1746-1763
Number of pages8
JournalJournal of the American Statistical Association
Issue number516
Early online date22 Dec 2015
Publication statusPublished - 4 Jan 2017

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