Sequential Poisson Regression in Diffusion Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00524262" target="_blank" >RIV/67985556:_____/20:00524262 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/20:00341229
Result on the web
<a href="https://ieeexplore.ieee.org/document/9066870" target="_blank" >https://ieeexplore.ieee.org/document/9066870</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/LSP.2020.2987723" target="_blank" >10.1109/LSP.2020.2987723</a>
Alternative languages
Result language
angličtina
Original language name
Sequential Poisson Regression in Diffusion Networks
Original language description
The Poisson regression is a popular model for positive integer random variables determined by known explanatory variables. This letter studies the problem of its collaborative Bayesian sequential estimation under potentially slowly time-varying regression coefficients. We assume networks where agents share their information about the inferred quantities with adjacent neighbors in order to improve the overall estimation performance. The communication strategy is the information diffusion, i.e., only one information exchange per time instant is allowed.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Signal Processing Letters
ISSN
1070-9908
e-ISSN
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Volume of the periodical
27
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
5
Pages from-to
625-629
UT code for WoS article
000536270500001
EID of the result in the Scopus database
2-s2.0-85087645141