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

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• 作者：Luca Martino^a ; ^{lukatotal@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jesse Read^b ; ^cAuthor Vitae ; Ví ; ctor Elvira^dAuthor Vitae ; Francisco Louzada^aAuthor Vitae
• 关键词：Sequential model selection ; Modality detection ; Marginal likelihood estimation ; Parallel particle filters ; Distributed inference ; Urban mobility
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：172-185
• 全文大小：1548 K

文摘

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.