Deep Neyman-Scott Processes

Chengkuan Hong, Christian Shelton
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:3627-3646, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-hong22a, title = { Deep Neyman-Scott Processes }, author = {Hong, Chengkuan and Shelton, Christian}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {3627--3646}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/hong22a/hong22a.pdf}, url = {https://proceedings.mlr.press/v151/hong22a.html}, 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. } }
Endnote
%0 Conference Paper %T Deep Neyman-Scott Processes %A Chengkuan Hong %A Christian Shelton %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-hong22a %I PMLR %P 3627--3646 %U https://proceedings.mlr.press/v151/hong22a.html %V 151 %X 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.
APA
Hong, C. & Shelton, C.. (2022). Deep Neyman-Scott Processes . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:3627-3646 Available from https://proceedings.mlr.press/v151/hong22a.html.

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