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Description
  • The paper studies the problem of decentralized distributed estimation of the state-space models from the Bayesian viewpoint. The adopted diffusion strategy, consisting of collective adaptation to new data and combination of posterior estimates, is derived in general model-independent form. Its particular application to the celebrated Kalman filter demonstrates the ease of use, especially when the measurement model is rewritten into the exponential family form and a conjugate prior describes the estimated state.
  • The paper studies the problem of decentralized distributed estimation of the state-space models from the Bayesian viewpoint. The adopted diffusion strategy, consisting of collective adaptation to new data and combination of posterior estimates, is derived in general model-independent form. Its particular application to the celebrated Kalman filter demonstrates the ease of use, especially when the measurement model is rewritten into the exponential family form and a conjugate prior describes the estimated state. (en)
Title
  • Diffusion Estimation Of State-Space Models: Bayesian Formulation
  • Diffusion Estimation Of State-Space Models: Bayesian Formulation (en)
skos:prefLabel
  • Diffusion Estimation Of State-Space Models: Bayesian Formulation
  • Diffusion Estimation Of State-Space Models: Bayesian Formulation (en)
skos:notation
  • RIV/67985556:_____/14:00431804!RIV15-GA0-67985556
http://linked.open...avai/riv/aktivita
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  • P(GP14-06678P)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 11510
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  • RIV/67985556:_____/14:00431804
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  • distributed estimation; state-space models; Bayesian estimation (en)
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http://linked.open...ontrolniKodProRIV
  • [46747012178A]
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  • Reims
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  • Reims
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  • Proceedings of the 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014)
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  • Dedecius, Kamil
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number of pages
http://bibframe.org/vocab/doi
  • 10.1109/MLSP.2014.6958920
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-1-4799-3693-9
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