6533b83afe1ef96bd12a7079
RESEARCH PRODUCT
Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range
Tuomas RajalaAntti Penttinensubject
Statistics and ProbabilityMathematical optimizationGaussianBayesian probabilityBayesian analysisMarkov processRegularization (mathematics)symbols.namesakeGaussian process regularisationPERFECT SIMULATIONRange (statistics)Statistical physicsGaussian processMathematicsta113ta112Random fieldApplied MathematicsInhomogeneousSand Martin's nestsTRANSFORMATIONHard-core point processComputational MathematicsTransformation (function)Computational Theory and MathematicssymbolsINFERENCEdescription
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) colony11Dataset is attached to the online version. on a vertical sand bank are analysed.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-03-01 | COMPUTATIONAL STATISTICS AND DATA ANALYSIS |