State Inference in Variational Bayesian Nonlinear State-Space Models
Identifieur interne : 000125 ( Main/Exploration ); précédent : 000124; suivant : 000126State Inference in Variational Bayesian Nonlinear State-Space Models
Auteurs : Tapani Raiko [Finlande] ; Matti Tornio [Finlande] ; Antti Honkela [Finlande] ; Juha Karhunen [Finlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: Nonlinear source separation can be performed by inferring the state of a nonlinear state-space model. We study and improve the inference algorithm in the variational Bayesian blind source separation model introduced by Valpola and Karhunen in 2002. As comparison methods we use extensions of the Kalman filter that are widely used inference methods in tracking and control theory. The results in stability, speed, and accuracy favour our method especially in difficult inference problems.
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DOI: 10.1007/11679363_28
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Box 5400, FI-02015, Espoo</wicri:regionArea><wicri:noRegion>Espoo</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Finlande</country></affiliation></author><author><name sortKey="Karhunen, Juha" sort="Karhunen, Juha" uniqKey="Karhunen J" first="Juha" last="Karhunen">Juha Karhunen</name><affiliation wicri:level="1"><country xml:lang="fr">Finlande</country><wicri:regionArea>Neural Networks Research Centre, Helsinki University of Technology, HUT, P.O. Box 5400, FI-02015, Espoo</wicri:regionArea><wicri:noRegion>Espoo</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Finlande</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2006</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">137E65C540D83D6DA0ADCAC6589FDB65972A385C</idno><idno type="DOI">10.1007/11679363_28</idno><idno type="ChapterID">28</idno><idno type="ChapterID">Chap28</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Nonlinear source separation can be performed by inferring the state of a nonlinear state-space model. We study and improve the inference algorithm in the variational Bayesian blind source separation model introduced by Valpola and Karhunen in 2002. As comparison methods we use extensions of the Kalman filter that are widely used inference methods in tracking and control theory. The results in stability, speed, and accuracy favour our method especially in difficult inference problems.</div></front></TEI><affiliations><list><country><li>Finlande</li></country></list><tree><country name="Finlande"><noRegion><name sortKey="Raiko, Tapani" sort="Raiko, Tapani" uniqKey="Raiko T" first="Tapani" last="Raiko">Tapani Raiko</name></noRegion><name sortKey="Honkela, Antti" sort="Honkela, Antti" uniqKey="Honkela A" first="Antti" last="Honkela">Antti Honkela</name><name sortKey="Honkela, Antti" sort="Honkela, Antti" uniqKey="Honkela A" first="Antti" last="Honkela">Antti Honkela</name><name sortKey="Karhunen, Juha" sort="Karhunen, Juha" uniqKey="Karhunen J" first="Juha" last="Karhunen">Juha Karhunen</name><name sortKey="Karhunen, Juha" sort="Karhunen, Juha" uniqKey="Karhunen J" first="Juha" last="Karhunen">Juha Karhunen</name><name sortKey="Raiko, Tapani" sort="Raiko, Tapani" uniqKey="Raiko T" first="Tapani" last="Raiko">Tapani Raiko</name><name sortKey="Tornio, Matti" sort="Tornio, Matti" uniqKey="Tornio M" first="Matti" last="Tornio">Matti Tornio</name><name sortKey="Tornio, Matti" sort="Tornio, Matti" uniqKey="Tornio M" first="Matti" last="Tornio">Matti Tornio</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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