Uncertainty in Artificial Intelligence
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Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Tom Gunter, Chris Lloyd, Michael Osborne, Stephen Roberts
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.
Keywords:
Pages: 310-319
PS Link:
PDF Link: /papers/14/p310-gunter.pdf
BibTex:
@INPROCEEDINGS{Gunter14,
AUTHOR = "Tom Gunter and Chris Lloyd and Michael Osborne and Stephen Roberts",
TITLE = "Efficient Bayesian Nonparametric Modelling of Structured Point Processes",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "310--319"
}


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