Efficient Bayesian Nonparametric Modelling of Structured Point Processes
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
Text
Gunter et al. "Efficient Bayesian Nonparametric Modelling of Structured Point Processes." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Gunter et al. "Efficient Bayesian Nonparametric Modelling of Structured Point Processes." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/gunter2014uai-efficient/)BibTeX
@inproceedings{gunter2014uai-efficient,
title = {{Efficient Bayesian Nonparametric Modelling of Structured Point Processes}},
author = {Gunter, Tom and Lloyd, Chris M. and Osborne, Michael A. and Roberts, Stephen J.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {310-319},
url = {https://mlanthology.org/uai/2014/gunter2014uai-efficient/}
}