Scalable inference for space-time Gaussian Cox processes. (English) Zbl 1435.62433
In this paper, the authors investigate an approach to point processes, a stochastic process that places points on some mathematical space. The authors use nearest-neighbor Gaussian processes with the exact space-time Gaussian Cox process. Inference is then accomplished using an MCMC process over. This leads to a probability model that can be used to predict the space-time locations of discrete point processes in the real world. The authors validate the model by analyzing crime data from San Francisco.
Reviewer: James P. Howard II (Columbia)
MSC:
| 62P25 | Applications of statistics to social sciences |
| 62H11 | Directional data; spatial statistics |
| 60G55 | Point processes (e.g., Poisson, Cox, Hawkes processes) |
| 60G15 | Gaussian processes |
Keywords:
Gaussian Cox processes; Gaussian processes; nearest neighbor Gaussian processes; Poisson thinning; space-time point patternReferences:
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