Modeling Events with Cascades of Poisson Processes
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 modeled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM algorithm can be implemented as a distributed algorithm, permitting the model to be applied to very large datasets. We apply these techniques to the modeling of Twitter messages and the revision history of Wikipedia.
Cite
Text
Simma and Jordan. "Modeling Events with Cascades of Poisson Processes." Conference on Uncertainty in Artificial Intelligence, 2010.Markdown
[Simma and Jordan. "Modeling Events with Cascades of Poisson Processes." Conference on Uncertainty in Artificial Intelligence, 2010.](https://mlanthology.org/uai/2010/simma2010uai-modeling/)BibTeX
@inproceedings{simma2010uai-modeling,
title = {{Modeling Events with Cascades of Poisson Processes}},
author = {Simma, Aleksandr and Jordan, Michael I.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2010},
pages = {546-555},
url = {https://mlanthology.org/uai/2010/simma2010uai-modeling/}
}