Modeling Events with Cascades of Poisson Processes

2012/03/15 by Aleksandr Simma, Michael I. Jordan, Simma, Aleksandr +1 · 2 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Simulation Techniques and Applications #cs.AI #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1203.3516

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

arxiv created 2012/03/15 · openalex publication_date 2012/03/15 · arxiv updated 2012/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• MapReduce

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