Cooperative parallel particle filters for online model selection and applications to urban mobility

Luca Martino*, Jesse Read, Víctor Elvira, Francisco Louzada

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

85 Citations (Scopus)

Abstract

We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.

Original languageEnglish
Pages (from-to)172-185
Number of pages14
JournalDigital Signal Processing
Volume60
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Distributed inference
  • Marginal likelihood estimation
  • Modality detection
  • Parallel particle filters
  • Sequential model selection
  • Urban mobility

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