Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods

ABSTRACT

The Log-Gaussian Cox process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly stochastic property, that is, it is a hierarchical combination of a Poisson process at the first level and a Gaussian process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data.

KEYWORDS:

• Hamiltonian Monte Carlo
• integrated nested Laplace approximation
• Log-Gaussian Cox process
• Variational Bayes

AMS CLASSIFICATION:

• 62M30

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Dr. Johnson would like to thank the National Institute of Neurological Disorders and Stroke for their financial support: NIH [grant number 5-R01-NS-075066]. Dr. Nathoo is supported by funding from the Natural Sciences and Engineering Research Council of Canada (2014-06542, 950-229356) and holds a Tier II Canada Research Chair in Biostatistics for Spatial and High-Dimensional Data. The work presented in this manuscript represents the views of the authors and not necessarily that of the NINDS, NIH, nor the NSEERC.