Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information. (English) Zbl 1514.62759
Summary: We investigate marked non-homogeneous Poisson processes using finite mixtures of bivariate normal components to model the spatial intensity function. We employ a Bayesian hierarchical framework for estimation of the parameters in the model, and propose an approach for including covariate information in this context. The methodology is exemplified through an application involving modeling of and inference for tornado occurrences.
MSC:
| 62-XX | Statistics |
Keywords:
birth-death Markov chain Monte Carlo; data augmentation; marked point processes; mixture models; non-homogeneous Poisson processSoftware:
INLAReferences:
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