Variational Bayes Approach for Classification of Points in Superpositions of Point Processes

Authors

Affiliations

Tuomas Rajala

Chalmers University of Technology and the University of Gothenburg

Claudia Redenbach

University of Kaiserslautern

Aila Särkkä

Chalmers University of Technology and the University of Gothenburg

Martina Sormani

University of Kaiserslautern

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.

Example noise point classification, not in the paper. The right-hand panel shows the VB noise-class probability traces.