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Göteborgs universitets publikationer

Variational Bayes approach for classification of points in superpositions of point processes

Författare och institution:
Tuomas Rajala (Institutionen för matematiska vetenskaper, Chalmers/GU); C. Redenbach (-); Aila Särkkä (Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers/GU); M. Sormani (-)
Publicerad i:
Spatial Statistics, 15 s. 85-99
ISSN:
2211-6753
Publikationstyp:
Artikel, refereegranskad vetenskaplig
Publiceringsår:
2016
Språk:
engelska
Fulltextlänk:
Sammanfattning (abstract):
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.
Ämne (baseras på Högskoleverkets indelning av forskningsämnen):
NATURVETENSKAP ->
Matematik
NATURVETENSKAP ->
Geovetenskap och miljövetenskap ->
Geologi
Nyckelord:
Spatial point process, Superposition, Bayesian inference, Markov chain Monte Carlo, Noise detection
Postens nummer:
235594
Posten skapad:
2016-04-29 13:54

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