Diffusion Estimation Of State-Space Models: Bayesian Formulation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F14%3A00431804" target="_blank" >RIV/67985556:_____/14:00431804 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/MLSP.2014.6958920" target="_blank" >http://dx.doi.org/10.1109/MLSP.2014.6958920</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MLSP.2014.6958920" target="_blank" >10.1109/MLSP.2014.6958920</a>

Result language
angličtina
Original language name
Diffusion Estimation Of State-Space Models: Bayesian Formulation
Original language 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.
Czech name
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Czech description
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Project
<a href="/en/project/GP14-06678P" target="_blank" >GP14-06678P: Distributed dynamic estimation in diffusion networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014)
ISBN
978-1-4799-3693-9
ISSN
—
e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Reims
Event location
Reims
Event date
Sep 21, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000393407800075

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