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Sparse approximate inference for spatio-temporal point process models

Journal Article


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Abstract


  • Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally 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.

Authors


  •   Cseke, Botond (external author)
  •   Zammit-Mangion, Andrew
  •   Heskes, Tom (external author)
  •   Sanguinetti, Guido (external author)

Publication Date


  • 2016

Citation


  • Cseke, B., Zammit-Mangion, A., Heskes, T. & Sanguinetti, G. (2016). Sparse approximate inference for spatio-temporal point process models. Journal of the American Statistical Association, 111 (516), 1746-1763.

Scopus Eid


  • 2-s2.0-85010634233

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6439&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5411

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 1746

End Page


  • 1763

Volume


  • 111

Issue


  • 516

Place Of Publication


  • United States

Abstract


  • Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally 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.

Authors


  •   Cseke, Botond (external author)
  •   Zammit-Mangion, Andrew
  •   Heskes, Tom (external author)
  •   Sanguinetti, Guido (external author)

Publication Date


  • 2016

Citation


  • Cseke, B., Zammit-Mangion, A., Heskes, T. & Sanguinetti, G. (2016). Sparse approximate inference for spatio-temporal point process models. Journal of the American Statistical Association, 111 (516), 1746-1763.

Scopus Eid


  • 2-s2.0-85010634233

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6439&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5411

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 1746

End Page


  • 1763

Volume


  • 111

Issue


  • 516

Place Of Publication


  • United States