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
Superposition
Spatial point process
Markov chain Monte Carlo
Författare
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 85-99Ämneskategorier
Matematik
Geologi
DOI
10.1016/j.spasta.2015.12.001