Skip to main content

Bayesian inference for Matérn repulsive processes

Buy Article:

$43.00 plus tax (Refund Policy)

In many applications involving point pattern data, the Poisson process assumption is unrealistic, with the data exhibiting a more regular spread. Such repulsion between events is exhibited by trees for example, because of competition for light and nutrients. Other examples include the locations of biological cells and cities, and the times of neuronal spikes. Given the many applications of repulsive point processes, there is a surprisingly limited literature developing flexible, realistic and interpretable models, as well as efficient inferential methods. We address this gap by developing a modelling framework around the Matérn type III repulsive process. We consider some extensions of the original Matérn type III process for both the homogeneous and the inhomogeneous cases. We also derive the probability density of this generalized Matérn process, allowing us to characterize the conditional distribution of the various latent variables, and leading to a novel and efficient Markov chain Monte Carlo algorithm. We apply our ideas to data sets of spatial locations of trees, nerve fibre cells and Greyhound bus stations.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Event process; Gaussian process; Gibbs sampling; Matérn process; Point pattern data; Poisson process; Repulsive process; Spatial data

Document Type: Research Article

Publication date: 2017-06-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more