Efficient Bayesian Nonparametric Modelling of Structured Point Processes

Tom Gunter, Chris Lloyd, Stephen Roberts, Michael A. Osborne
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:765-774, 2014.

Abstract

This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-gunter14a, title = {Efficient {B}ayesian Nonparametric Modelling of Structured Point Processes}, author = {Gunter, Tom and Lloyd, Chris and Roberts, Stephen and Osborne, Michael A.}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {765--774}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/gunter14a/gunter14a.pdf}, url = {https://proceedings.mlr.press/r12/gunter14a.html}, abstract = {This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Efficient Bayesian Nonparametric Modelling of Structured Point Processes %A Tom Gunter %A Chris Lloyd %A Stephen Roberts %A Michael A. Osborne %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-gunter14a %I PMLR %P 765--774 %U https://proceedings.mlr.press/r12/gunter14a.html %V R12 %X This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach. %Z Reissued by PMLR on 04 October 2026.
APA
Gunter, T., Lloyd, C., Roberts, S. & Osborne, M.A.. (2014). Efficient Bayesian Nonparametric Modelling of Structured Point Processes. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:765-774 Available from https://proceedings.mlr.press/r12/gunter14a.html. Reissued by PMLR on 04 October 2026.

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