Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility

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.

arXiv commentA preliminary Matlab code is provided at http://www.mathworks.com/matlabcentral/fileexchange/58597-model-averanging-particle-filter, Digital Signal Processing, 2016

References in corpus (3)

• Effective Sample Size for Importance Sampling based on discrepancy measures
• Efficient Multiple Importance Sampling Estimators
• Adapting the Number of Particles in Sequential Monte Carlo Methods through an Online Scheme for Convergence Assessment

Cited by in corpus (13)

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• A Survey of Monte Carlo Methods for Parameter Estimation
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• Marginal likelihood computation for model selection and hypothesis testing: an extensive review
• Group Importance Sampling for Particle Filtering and MCMC
• Compressed Monte Carlo with application in particle filtering
• On the safe use of prior densities for Bayesian model selection
• Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models
• Deep Importance Sampling based on Regression for Model Inversion and Emulation
• Regime Learning for Differentiable Particle Filters
• Data-Driven Model Set Design for Model Averaged Particle Filter
• Optimality in Noisy Importance Sampling