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Abstract:
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Bayesian nonparametric methods have been previously employed in flexible modeling for spatial data that may be viewed as the realization of a Poisson point process. When these events occur over time, it is sometimes possible to incorporate temporal dependence by viewing the data as a single realization of a point process which occurs in both space and time. However, in discrete time settings---when each time point corresponds to a set of spatially distributed events---it is more appropriate to model the data as arising from multiple point processes that have an intensity function which is changing in time. This is made possible by employing a novel version of the Dependent Dirichlet process as a prior for the time-indexed normalized process intensities. Details of the development of the model and prior measure will be provided, along with an example involving crime event data.
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