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

Tuomas Rajala (Institutionen för matematiska vetenskaper, matematisk statistik) ; C. Redenbach ; Aila Särkkä (Institutionen för matematiska vetenskaper, matematisk statistik) ; M. Sormani
Spatial Statistics (2211-6753). Vol. 15 (2016), p. 85-99.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Spatial point process, Superposition, Bayesian inference, Markov chain Monte Carlo, Noise detection

Denna post skapades 2016-04-29. Senast ändrad 2016-10-26.
CPL Pubid: 235594


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)



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