Cooperative parallel particle filters for online model selection and applications to urban mobility
Cooperative parallel particle filters for online model selection and applications to urban mobility
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DOI：
10.1016/j.dsp.2016.09.011
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发表时间：
2017-01-01
影响因子：
2.9
通讯作者：
Louzada, Francisco
中科院分区：
文献类型：
--
作者：
Martino, Luca;Read, Jesse;Louzada, Francisco
我们设计了一个序贯蒙特卡罗方案的贝叶斯推理和模型选择的双重目的。我们认为，城市交通的应用背景下，可以采用几种方式的运输和不同的测量设备。因此，我们解决了当前模态的在线跟踪和检测的联合问题。为此，我们使用相互作用的并行粒子滤波器，每个粒子滤波器处理不同的模型。它们合作提供感兴趣变量的全局估计，同时提供给定数据的每个模型的后验密度的近似。的相互作用发生的计算工作的一个吝啬的分布，与在线适应的粒子数的每个过滤器根据相应的模型的后验概率。所得到的方案简单灵活。我们已经测试了新的技术在不同的数值实验与人工和真实的数据，这证实了所提出的计划的鲁棒性。(C)2016 Elsevier Inc. All rights reserved.
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. (C) 2016 Elsevier Inc. All rights reserved.