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

T. Rajala*, A. Penttinen

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    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
    Volume71
    DOIs
    Publication statusPublished - Mar 2014
    MoE publication typeA1 Journal article-refereed

    Keywords

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

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