bibtype	J - Journal Article
ARLID	0467560
utime	20240103213206.8
mtime	20161220235959.9
SCOPUS	85014905526
WOS	000395484200012
DOI	10.1109/TSP.2016.2641380
title (primary) (eng)	Sequential estimation and diffusion of information over networks: A Bayesian approach with exponential family of distributions
specification	page_count 16 s. media_type E
serial	ARLID cav_un_epca*0256727 ISSN 1053-587X title IEEE Transactions on Signal Processing volume_id 65 volume 7 (2017) page_num 1795-1809
keyword	diffusion network
keyword	diffusion estimation
keyword	adaptation
keyword	combination
keyword	exponential family
author (primary)	ARLID cav_un_auth*0242543 name1 Dedecius name2 Kamil full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS institution UTIA-B full_dept Department of Adaptive Systems country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0306051 name1 Djurić name2 P. M. country US
source	url http://library.utia.cas.cz/separaty/2016/AS/dedecius-0467560.pdf
cas_special	project ARLID cav_un_auth*0303543 project_id GP14-06678P agency GA ČR country CZ abstract (eng) Diffusion networks where nodes collaboratively estimate the parameters of stochastic models from shared observations and other estimates have become an established research topic. In this paper the problem of sequential estimation where information in the network diffuses with time is formulated abstractly and independently from any particular model. The objective is to reach a generic solution that is applicable to a wide class of popular models and based on the exponential family of distributions. The adopted Bayesian and information-theoretic paradigms provide probabilistically consistent means for incorporation of shared observations in the implemented estimation of the unknowns by the nodes as well as for effective combination of the „knowledge“ of the nodes over the network. It is shown and illustrated on four examples that under certain conditions, the resulting algorithms are analytically tractable, either directly or after simple approximations. The examples include the linear regression, Kalman filtering, logistic regression, and the inference of an inhomogeneous Poisson process. The first two examples have their more or less direct counterparts in the state-of-the-art diffusion literature whereas the latter two are new. result_subspec WOS RIV BD FORD0 10000 FORD1 10100 FORD2 10102 reportyear 2018 num_of_auth 2 mrcbC52 4 A hod 4ah 20231122142123.0 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0266434 mrcbC64 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS confidential S mrcbC86 1 Article Engineering Electrical Electronic mrcbC86 1 Article Engineering Electrical Electronic mrcbC86 1 Article Engineering Electrical Electronic mrcbT16-e ENGINEERINGELECTRICALELECTRONIC mrcbT16-j 1.559 mrcbT16-s 1.247 mrcbT16-B 87.621 mrcbT16-D Q1 mrcbT16-E Q1* arlyear 2017 mrcbTft \nSoubory v repozitáři: dedecius-0467560.pdf mrcbU14 85014905526 SCOPUS mrcbU24 PUBMED mrcbU34 000395484200012 WOS mrcbU63 cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 65 č. 7 2017 1795 1809