Modeling Events with Cascades of Poisson Processes

Aleksandr Simma, Michael Jordan
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:545-554, 2010.

Abstract

We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby mod- eled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM al- gorithm can be implemented as a distributed algorithm, permitting the model to be ap- plied to very large datasets. We apply these techniques to the modeling of Twitter mes- sages and the revision history of Wikipedia.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-simma10a, title = {Modeling Events with Cascades of {P}oisson Processes}, author = {Simma, Aleksandr and Jordan, Michael}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {545--554}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/simma10a/simma10a.pdf}, url = {https://proceedings.mlr.press/r8/simma10a.html}, abstract = {We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby mod- eled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM al- gorithm can be implemented as a distributed algorithm, permitting the model to be ap- plied to very large datasets. We apply these techniques to the modeling of Twitter mes- sages and the revision history of Wikipedia.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Modeling Events with Cascades of Poisson Processes %A Aleksandr Simma %A Michael Jordan %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-simma10a %I PMLR %P 545--554 %U https://proceedings.mlr.press/r8/simma10a.html %V R8 %X We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby mod- eled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM al- gorithm can be implemented as a distributed algorithm, permitting the model to be ap- plied to very large datasets. We apply these techniques to the modeling of Twitter mes- sages and the revision history of Wikipedia. %Z Reissued by PMLR on 04 October 2026.
APA
Simma, A. & Jordan, M.. (2010). Modeling Events with Cascades of Poisson Processes. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:545-554 Available from https://proceedings.mlr.press/r8/simma10a.html. Reissued by PMLR on 04 October 2026.

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