Bayesian Nonparametric PoissonProcess Allocation for TimeSequence Modeling
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Proceedings of the TwentyFirst International Conference on Artificial Intelligence and Statistics, PMLR 84:11081116, 2018.
Abstract
Analyzing the underlying structure of multiple timesequences provides insights into the understanding of social networks and human activities. In this work, we present the Bayesian nonparametric Poisson process allocation (BaNPPA), a latentfunction model for timesequences, which automatically infers the number of latent functions. We model the intensity of each sequence as an infinite mixture of latent functions, each of which is obtained using a function drawn from a Gaussian process. We show that a technical challenge for the inference of such mixture models is the unidentifiability of the weights of the latent functions. We propose to cope with the issue by regulating the volume of each latent function within a variational inference algorithm. Our algorithm is computationally efficient and scales well to large data sets. We demonstrate the usefulness of our proposed model through experiments on both synthetic and realworld data sets.
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