Khan, A.M., De Freitas, A., Mihaylova, L.S. orcid.org/0000-0001-5856-2223 et al. (2 more authors) (2017) Bayesian Processing of Big Data using Log Homotopy Based Particle Flow Filters. In: 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF). The 11th Symposium Sensor Data Fusion: Trends, Solutions, and Applications, 10-12 Oct 2017, Bonn, Germany. IEEE ISBN 978-1-5386-3103-4
Abstract
Bayesian recursive estimation using large volumes of data is a challenging research topic. The problem becomes particularly complex for high dimensional non-linear state spaces. Markov chain Monte Carlo (MCMC) based methods have been successfully used to solve such problems. The main issue when employing MCMC is the evaluation of the likelihood function at every iteration, which can become prohibitively expensive to compute. Alternative methods are therefore sought after to overcome this difficulty. One such method is the adaptive sequential MCMC (ASMCMC), where the use of the confidence sampling is proposed as a method to reduce the computational cost. The main idea is to make use of the concentration inequalities to sub-sample the measurements for which the likelihood terms are evaluated. However, ASMCMC methods require appropriate proposal distributions. In this work, we propose a novel ASMCMC framework in which log-homotopy based particle flow filters form adaptive proposals. We show the performance can be significantly enhanced by our proposed algorithm, while still maintaining a comparatively low processing overhead.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Particle flow filters; Log-homotopy; DHF; big data; SMCMC; Confidence sampling; Multiple target tracking |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 TRAX - 607400 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Sep 2017 09:59 |
Last Modified: | 21 Jun 2018 14:12 |
Published Version: | https://doi.org/10.1109/SDF.2017.8126349 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/SDF.2017.8126349 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121517 |