Latent point process allocation

Conference item

We introduce a probabilistic model for the factorisation of continuous Poisson process rate functions. Our model can be thought of as a topic model for Poisson point processes in which each point is assigned to one of a set of latent rate functions that are shared across multiple outputs. We show that the model brings a means of incorporating structure in point process inference beyond the state-of-the-art. We derive an efficient variational inference scheme for the model based on sparse Gaussian processes that scales linearly in the number of data points. Finally, we demonstrate, using examples from spatial and temporal statistics, how the model can be used for discovering hidden structure with greater precision than standard frequentist approaches.

Authors: Lloyd, C; Gunter, T; Nickson, T; Osborne, M; Roberts, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Host title: JMLR: Workshop and Conference Proceedings: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS)
• Journal: JMLR: Workshop and Conference Proceedings More from this journal
• Volume: 41
• Pages: 389-397
• Publication date: 2016-01-01
• Acceptance date: 2015-10-09
• Event location: Cadiz
• Event start date: 2016-05-09
• Event end date: 2016-05-11
• ISSN: 1938-7228