Deep Neyman-Scott Processes (2022)

by Chengkuan Hong and Christian R. Shelton

Abstract: A Neyman-Scott process is a special case of a Cox process. The latent and observable stochastic processes are both Poisson processes. We consider a deep Neyman-Scott process in this paper, for which the building components of a network are all Poisson processes. We develop an efficient posterior sampling via Markov chain Monte Carlo and use it for likelihood-based inference. Our method opens up room for the inference in sophisticated hierarchical point processes. We show in the experiments that more hidden Poisson processes brings better performance for likelihood fitting and events types prediction. We also compare our method with state-of-the-art models for temporal real-world datasets and demonstrate competitive abilities for both data fitting and prediction, using far fewer parameters.

Download Information

Chengkuan Hong and Christian R. Shelton (2022). "Deep Neyman-Scott Processes." International Conference on Artificial Intelligence and Statistics.

Bibtex citation

@inproceedings{HonShe22,
     author = "Chengkuan Hong and Christian R. Shelton",
     title = "Deep {N}eyman-{S}cott Processes",
     year = 2022,
     booktitle = "International Conference on Artificial Intelligence and Statistics",
     booktitleabbr = "AISTATS",
}

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