Modeling event cascades using networks of additive count sequences

Abstract

We propose a statistical model for networks of event count sequences built on a cascade structure. We assume that each event triggers successor events, whose counts follow additive probability distributions; the ensemble of counts is given by their superposition. These assumptions allow the marginal distribution of count sequences and the conditional distribution of event cascades to take analytic forms. We present our model framework using Poisson and negative binomial distributions as the building blocks. Based on this formulation, we describe a statistical method for estimating the model parameters and event cascades from the observed count sequences.