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dc.contributor.authorLi, Chenhao
dc.contributor.authorGodsill, Simon
dc.date.accessioned2021-02-16T00:31:24Z
dc.date.available2021-02-16T00:31:24Z
dc.date.issued2021
dc.identifier.issn1520-6149
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317666
dc.description.abstractParameter learning of the state-space model (SSM) plays a significant role in the modelling of time-series data and dynamical systems. However, the closed-form inference of the parameter posterior is often limited by sequential construction and non-linearity of the SSMs, which has led to the development of sampling-based algorithms such as particle Markov chain Monte Carlo (PMCMC). We present a novel algorithm, the particle filter variational inference (PF-VI) algorithm, which achieves closed-form learning of SSM parameters while tractably inferring the non-linear sequential states. We apply the algorithm to a popular non-linear SSM example and compare its performance against two competing PMCMC algorithms.
dc.publisherIEEE
dc.rightsAll rights reserved
dc.titleVariational parameter learning in sequential state-space model via particle filtering
dc.typeConference Object
prism.endingPage5593
prism.publicationDate2021
prism.publicationNameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
prism.startingPage5589
prism.volume2021-June
dc.identifier.doi10.17863/CAM.64781
dcterms.dateAccepted2021-01-30
rioxxterms.versionofrecord10.1109/ICASSP39728.2021.9414033
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-01-01
dc.contributor.orcidLi, Chenhao [0000-0001-6731-3675]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.conference-nameICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
pubs.conference-start-date2021-06-06
cam.orpheus.successMon Jun 07 07:32:34 BST 2021 - Embargo updated
cam.orpheus.counter15
pubs.conference-finish-date2021-06-11
rioxxterms.freetoread.startdate2022-06-06


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