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 mod- elled independently. We can handle miss- ing data naturally, infer latent structure, and cope with large numbers of observed pro- cesses. A further novel contribution enables the model to work effectively in higher dimen- sional 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 mod- elled independently. We can handle miss- ing data naturally, infer latent structure, and cope with large numbers of observed pro- cesses. A further novel contribution enables the model to work effectively in higher dimen- sional 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 mod- elled independently. We can handle miss- ing data naturally, infer latent structure, and cope with large numbers of observed pro- cesses. A further novel contribution enables the model to work effectively in higher dimen- sional 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.

Related Material