Variational Bayes approach for classification of points in superpositions of point processes
Journal article, 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.
Noise detection
Spatial point process
Superposition
Bayesian inference
Markov chain Monte Carlo
Author
Tuomas Rajala
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
Claudia Redenbach
Technische Universität Kaiserslautern
Aila Särkkä
University of Gothenburg
Chalmers, Mathematical Sciences, Mathematical Statistics
Martina Sormani
Technische Universität Kaiserslautern
Fraunhofer Society
Published in
Spatial Statistics
2211-6753 (ISSN)
Vol. 15 p. 85-99Categorizing
Subject Categories (SSIF 2011)
Mathematics
Geology
Identifiers
DOI
10.1016/j.spasta.2015.12.001