{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T13:34:05Z","timestamp":1730295245304,"version":"3.28.0"},"reference-count":11,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1109\/sdf.2017.8126349","type":"proceedings-article","created":{"date-parts":[[2017,12,4]],"date-time":"2017-12-04T22:25:04Z","timestamp":1512426304000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian processing of big data using log homotopy based particle flow filters"],"prefix":"10.1109","author":[{"given":"Muhammad Altamash","family":"Khan","sequence":"first","affiliation":[{"name":"Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany"}]},{"given":"Allan","family":"De Freitas","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom"}]},{"given":"Lyudmila","family":"Mihaylova","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom"}]},{"given":"Martin","family":"Ulmke","sequence":"additional","affiliation":[{"name":"Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany"}]},{"given":"Wolfgang","family":"Koch","sequence":"additional","affiliation":[{"name":"Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/TPAMI.2005.223"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1063\/1.1699114"},{"key":"ref10","first-page":"405","article-title":"Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach","author":"bardenet","year":"2014","journal-title":"Proceedings of the 31st International Conference on Machine Learning (ICML-14) JMLR Workshop and Conference Proceedings"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/CAMSAP.2009.5413256"},{"key":"ref11","article-title":"Analysis of Log-Homotopy based Particle Flow Filters","volume":"12","author":"khan","year":"2017","journal-title":"J Adv Inform Fusion"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/ISSNIP.2015.7106901"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/TAES.2016.150471"},{"key":"ref7","first-page":"230","article-title":"Expected Likelihood for Tracking in Clutter with Particle Filters","author":"marrs","year":"2002","journal-title":"Proceedings of SPIE Signal and Data Processing of Small Targets"},{"key":"ref2","first-page":"1","article-title":"On Markov chain Monte Carlo methods for tall data","volume":"18","author":"bardenet","year":"2017","journal-title":"Journal of Machine Learning Research"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/TAES.2008.4655362"},{"year":"0","author":"freitas","journal-title":"Sequential Markov Chain Monte Carlo for Bayesian Filtering with Massive Data","key":"ref1"}],"event":{"name":"2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","start":{"date-parts":[[2017,10,10]]},"location":"Bonn, Germany","end":{"date-parts":[[2017,10,12]]}},"container-title":["2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8118201\/8126346\/08126349.pdf?arnumber=8126349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T21:45:37Z","timestamp":1623102337000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8126349\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10]]},"references-count":11,"URL":"https:\/\/doi.org\/10.1109\/sdf.2017.8126349","relation":{},"subject":[],"published":{"date-parts":[[2017,10]]}}}