1. Brunel University Research Archive
  2. College of Engineering, Design and Physical Sciences
  3. Dept of Electronic and Electrical Engineering
  4. Dept of Electronic and Electrical Engineering Research Papers
Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23792
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLai, Y-
dc.contributor.authorGuan, W-
dc.contributor.authorLuo, L-
dc.contributor.authorRuan, Q-
dc.contributor.authorPing, Y-
dc.contributor.authorSong, H-
dc.contributor.authorMeng, H-
dc.contributor.authorPan, Y-
dc.date.accessioned2021-12-21T11:04:20Z-
dc.date.available2021-01-01-
dc.date.available2021-12-21T11:04:20Z-
dc.date.issued2021-10-25-
dc.identifier.citationLai, Y, Guan, W, Luo, L, Ruan, Q., Ping, Y., Song, H., Meng, H. and Pan, Y. (2021) 'Extended variational inference for Dirichlet process mixture of Beta-Liouville distributions for proportional data modeling', International Journal of Intelligent Systems, 0 (in press), pp. 1- 30. doi: 10.1002/int.22721.en_US
dc.identifier.issn0884-8173-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/23792-
dc.description.sponsorshipThe General Project of Science and Technology Plan of Beijing Municipal Commission of Education. Grant Number: KM201910009014; Shanghai Planning Office of Philosophy and Social Science. Grant Number: 2019EGL018; The Fundamental Research Funds for the Central Universities. Grant Number: 2020RC38; Key Technologies R & D Program of He'nan Province. Grant Number: 212102210084; The National Natural Science Foundation of China (NSFC). Grant Number: 71942003.en_US
dc.format.extent1 - 30 (30)-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherWileyen_US
dc.subjectbayesian estimationen_US
dc.subjectbeta-Liouville distributionen_US
dc.subjectdirichlet processen_US
dc.subjectextended variational inferenceen_US
dc.subjectinfinite mixture modelen_US
dc.subjectobject detectionen_US
dc.subjecttext categorizationen_US
dc.titleExtended variational inference for Dirichlet process mixture of Beta-Liouville distributions for proportional data modelingen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1002/int.22721-
dc.relation.isPartOfInternational Journal of Intelligent Systems-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn1098-111X-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfEmbargoed until 25 Oct 20225.94 MBAdobe PDFView/Open
Show simple item record


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.