Variational Bayes approach for classification of points in superpositions of point processes
Artikel i vetenskaplig tidskrift, 2016

We investigate the problem of classifying superpositions of spatial point processes. In particular, we are interested in realizations formed as a superposition of a regular point process and a Poisson point process. The aim is to decide which of the two processes each point belongs to. Recently, a Markov chain Monte Carlo (MCMC) approach was suggested by Redenbach et al. (2015), which however, is computationally heavy. In this paper, we will introduce a method based on variational Bayes approximation and compare its performance to the performance of a slightly refined version of the MCMC approach.

Bayesian inference

Noise detection


Spatial point process

Markov chain Monte Carlo


Tuomas Rajala

Chalmers, Matematiska vetenskaper, Matematisk statistik

Göteborgs universitet

Claudia Redenbach

Technische Universität Kaiserslautern

Aila Särkkä

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Matematisk statistik

Martina Sormani

Fraunhofer-Institut fur Techno- und Wirtschaftsmathematk

Technische Universität Kaiserslautern

Spatial Statistics

2211-6753 (ISSN)

Vol. 15 85-99






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