Bayesian estimation of unknown parameters over networks
Result description
We address the problem of sequential parameter estimation over networks using the Bayesian methodology. Each node sequentially acquires independent observations, where all the observations in the network contain signal(s) with unknown parameters. The nodes aim at obtaining accurate estimates of the unknown parameters and to that end, they collaborate with their neighbors. They communicate to the neighbors their latest posterior distributions of the unknown parameters. The nodes fuse the received information by using mixtures with weights proportional to the predictive distributions obtained from the respective node posteriors. Then they update the fused posterior using the next acquired observation, and the process repeats. We demonstrate the performance of the proposed approach with computer simulations and confirm its validity
Keywords
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Bayesian estimation of unknown parameters over networks
Original language description
We address the problem of sequential parameter estimation over networks using the Bayesian methodology. Each node sequentially acquires independent observations, where all the observations in the network contain signal(s) with unknown parameters. The nodes aim at obtaining accurate estimates of the unknown parameters and to that end, they collaborate with their neighbors. They communicate to the neighbors their latest posterior distributions of the unknown parameters. The nodes fuse the received information by using mixtures with weights proportional to the predictive distributions obtained from the respective node posteriors. Then they update the fused posterior using the next acquired observation, and the process repeats. We demonstrate the performance of the proposed approach with computer simulations and confirm its validity
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
GP14-06678P: Distributed dynamic estimation in diffusion networks
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proc. 2016 24th European Signal Processing Conference (EUSIPCO)
ISBN
978-0-9928-6266-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1508-1512
Publisher name
EUSIPCO
Place of publication
Budapest
Event location
Budapest
Event date
Aug 29, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000391891900289
Basic information
Result type
D - Article in proceedings
CEP
BB - Applied statistics, operational research
Year of implementation
2016