Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range

Research output: Contribution to journalArticle


  • T. Rajala
  • A. Penttinen

Research units

  • University of Jyväskylä


A Bayesian solution is suggested for the modelling of spatial point patterns with inhomogeneous hard-core radius using Gaussian processes in the regularization. The key observation is that a straightforward use of the finite Gibbs hard-core process likelihood together with a log-Gaussian random field prior does not work without penalisation towards high local packing density. Instead, a nearest neighbour Gibbs process likelihood is used. This approach to hard-core inhomogeneity is an alternative to the transformation inhomogeneous hard-core modelling. The computations are based on recent Markovian approximation results for Gaussian fields. As an application, data on the nest locations of Sand Martin (Riparia riparia) colony(1) on a vertical sand bank are analysed. (C) 2012 Elsevier B.V. All rights reserved.


Original languageEnglish
Pages (from-to)530-541
Number of pages12
JournalComputational Statistics and Data Analysis
Publication statusPublished - Mar 2014
MoE publication typeA1 Journal article-refereed

    Research areas

  • Hard-core point process, Inhomogeneous, Gaussian process regularisation, Bayesian analysis, Sand Martin's nests, PERFECT SIMULATION, INFERENCE, TRANSFORMATION

ID: 9683695