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.
Simma et al. REF consider a model in which a Poisson process triggers other Poisson processes.
Modeling Events with Cascades of Poisson Processes
ArXiv
ArXiv
Mathematics Computer Science