Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. (arXiv:2201.05044v1 [stat.ML])

Neyman-Scott process (NSP) are point process models that generate clusters of
points in time or space. They are natural models for a wide range of phenomena,
ranging from neural spike trains to document streams. The clustering property
is achieved via a doubly stochastic formulation: first, a set of latent events
is drawn from a Poisson process; then, each latent event generates a set of
observed data points according to another Poisson process. This construction is
similar to Bayesian nonparametric mixture …

arxiv bayesian clustering ml processes