Scalable inference for space-time Gaussian Cox processes

Shinichiro Shirota, Sudipto Banerjee

Research output: Contribution to journalArticlepeer-review

Abstract

The log-Gaussian Cox process is a flexible and popular stochastic process for modeling point patterns exhibiting spatial and space-time dependence. Model fitting requires approximation of stochastic integrals which is implemented through discretization over the domain of interest. With fine scale discretization, inference based on Markov chain Monte Carlo is computationally burdensome because of the cost of matrix decompositions and storage, such as the Cholesky, for high dimensional covariance matrices associated with latent Gaussian variables. This article addresses these computational bottlenecks by combining two recent developments: (i) a data augmentation strategy that has been proposed for space-time Gaussian Cox processes that is based on exact Bayesian inference and does not require fine grid approximations for infinite dimensional integrals, and (ii) a recently developed family of sparsity-inducing Gaussian processes, called nearest-neighbor Gaussian processes, to avoid expensive matrix computations. Our inference is delivered within the fully model-based Bayesian paradigm and does not sacrifice the richness of traditional log-Gaussian Cox processes. We apply our method to crime event data in San Francisco and investigate the recovery of the intensity surface.

Original languageEnglish
Pages (from-to)269-287
Number of pages19
JournalJournal of Time Series Analysis
Volume40
Issue number3
DOIs
Publication statusPublished - May 2019

Keywords

  • Gaussian Cox processes
  • Gaussian processes
  • Poisson thinning
  • nearest neighbor Gaussian processes
  • space-time point pattern

Fingerprint Dive into the research topics of 'Scalable inference for space-time Gaussian Cox processes'. Together they form a unique fingerprint.

Cite this