Classification of Points in Superpositions of Strauss and Poisson Processes
Artikel i vetenskaplig tidskrift, 2015

Consider a realisation of a point process which is formed as a superposition of a regular point process, here a Strauss process, and some Poisson noise. The aim of the current work is to decide which of the two processes each point belongs to. We construct an MCMC algorithm which estimates the parameters of the superposition model and obtains posterior probabilities for each point of being a Strauss point. The algorithm is evaluated in a simulation study. Finally, it is applied to our motivating data set containing the locations of air bubbles, some of which are noise, in an Antarctic ice core.

Markov chain Monte Carlo

Spatial point process

Bayesian inference

Noise removal

Parameter estimation

Noise detection


Claudia Redenbach

Technische Universitat Kaiserslautern

Aila Särkkä

Chalmers University of Technology

Aila Särkkä

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

Spatial Statistics

2211-6753 (ISSN)

Vol. 12 81-95


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