Variational Inference for Gaussian Processes with Panel Count Data

Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:289-298, 2018.

Abstract

We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at discrete time points and only the numbers of occurrences of the events between subsequent observation times are available. The exact occurrence timestamps of the events are un-known. The method of conducting the efficient variational inference is presented, based on the assumption of a Gaussian-process-modulated intensity function. We derive a tractable lower bound to alleviate the problems of the intractable evidence lower bound inherent in the variational inference framework. Our algorithm outperforms classical methods on both synthetic and three real panel count sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-ding18a, title = {Variational Inference for {G}aussian Processes with Panel Count Data}, author = {Ding, Hongyi and Lee, Young and Sato, Issei and Sugiyama, Masashi}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {289--298}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/ding18a/ding18a.pdf}, url = {https://proceedings.mlr.press/r16/ding18a.html}, abstract = {We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at discrete time points and only the numbers of occurrences of the events between subsequent observation times are available. The exact occurrence timestamps of the events are un-known. The method of conducting the efficient variational inference is presented, based on the assumption of a Gaussian-process-modulated intensity function. We derive a tractable lower bound to alleviate the problems of the intractable evidence lower bound inherent in the variational inference framework. Our algorithm outperforms classical methods on both synthetic and three real panel count sets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Variational Inference for Gaussian Processes with Panel Count Data %A Hongyi Ding %A Young Lee %A Issei Sato %A Masashi Sugiyama %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-ding18a %I PMLR %P 289--298 %U https://proceedings.mlr.press/r16/ding18a.html %V R16 %X We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at discrete time points and only the numbers of occurrences of the events between subsequent observation times are available. The exact occurrence timestamps of the events are un-known. The method of conducting the efficient variational inference is presented, based on the assumption of a Gaussian-process-modulated intensity function. We derive a tractable lower bound to alleviate the problems of the intractable evidence lower bound inherent in the variational inference framework. Our algorithm outperforms classical methods on both synthetic and three real panel count sets. %Z Reissued by PMLR on 04 October 2026.
APA
Ding, H., Lee, Y., Sato, I. & Sugiyama, M.. (2018). Variational Inference for Gaussian Processes with Panel Count Data. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:289-298 Available from https://proceedings.mlr.press/r16/ding18a.html. Reissued by PMLR on 04 October 2026.

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