Modeling event cascades using networks of additive count sequences
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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 the count sequences and the conditional distribution of the event cascades to have analytic forms. We present our model framework using Poisson and negative binomial distributions as the building blocks. Based on this, we describe a statistical method for estimating the model parameters and the event cascades from the observed count sequences.
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