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Fast Convergence Identification of Hidden Markov Models using Risk-Sensitive Filters

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dc.contributor.authorThorne, Jeremy
dc.contributor.authorMoore, John
dc.date.accessioned2015-12-13T22:15:33Z
dc.date.issued2001
dc.date.updated2015-12-11T07:18:12Z
dc.description.abstractIn this paper we derive recursive risk-sensitive filters which may be used for both on-line and off-line identification of hidden Markov models (HMMs). The identification is achieved by first taking risk-sensitive conditional mean estimates of the number
dc.identifier.issn0362-546X
dc.identifier.urihttp://hdl.handle.net/1885/70462
dc.publisherPergamon-Elsevier Ltd
dc.sourceNonlinear Analysis
dc.subjectKeywords: Computer simulation; Convergence of numerical methods; Mathematical models; Optimization; Parameter estimation; Hidden Markov models (HMM); Risk-sensitive filters; Markov processes Convergence; Hidden Markov models; Identification; Risk-sensitive
dc.titleFast Convergence Identification of Hidden Markov Models using Risk-Sensitive Filters
dc.typeJournal article
local.bibliographicCitation.issue4
local.bibliographicCitation.lastpage2472
local.bibliographicCitation.startpage2461
local.contributor.affiliationThorne, Jeremy, College of Engineering and Computer Science, ANU
local.contributor.affiliationMoore, John, College of Engineering and Computer Science, ANU
local.contributor.authoruidThorne, Jeremy, u9802202
local.contributor.authoruidMoore, John, u8202879
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010406 - Stochastic Analysis and Modelling
local.identifier.ariespublicationMigratedxPub2318
local.identifier.citationvolume47
local.identifier.doi10.1016/S0362-546X(01)00369-8
local.identifier.scopusID2-s2.0-0035420957
local.type.statusPublished Version

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