Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
This work addresses time-sequence analysis for social networks and human activities, presenting an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of modeling multiple time-sequences to understand social networks and human activities by introducing Bayesian nonparametric Poisson process allocation (BaNPPA), which infers latent functions and addresses unidentifiability issues, showing computational efficiency and scalability in experiments.
Analyzing the underlying structure of multiple time-sequences provides insights into the understanding of social networks and human activities. In this work, we present the \emph{Bayesian nonparametric Poisson process allocation} (BaNPPA), a latent-function model for time-sequences, 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 real-world data sets.