Modeling Events with Cascades of Poisson Processes

Understand

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.