Factorized Estimation of Partially Shared Parameters in Diffusion Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00477044" target="_blank" >RIV/67985556:_____/17:00477044 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TSP.2017.2725226" target="_blank" >http://dx.doi.org/10.1109/TSP.2017.2725226</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2017.2725226" target="_blank" >10.1109/TSP.2017.2725226</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Factorized Estimation of Partially Shared Parameters in Diffusion Networks

Popis výsledku v původním jazyce

Collaborative estimation of partially common parameters over ad hoc diffusion networks where the nodes directly communicate with their neighbors is a challenging task. The problem complexity is significantly high under the lack of knowledge which parameters are shared and among which network nodes. In this paper, we propose an adaptive framework suitable for this task. It is abstractly formulated in the Bayesian and information-theoretic paradigms and, therefore, versatile and easily applicable to a relatively wide class of models. If the observation models belong to the exponential family and the same functional types of prior probability distributions are used for estimation of the shared parameters, the method reduces to an analytically tractable variational algorithm extended by a procedure that passes messages among network nodes. A simulation example demonstrates that the collaboration improves estimation performance of both the shared and strictly local parameters, compared with the noncollaborative scenario.

Název v anglickém jazyce

Factorized Estimation of Partially Shared Parameters in Diffusion Networks

Popis výsledku anglicky

Collaborative estimation of partially common parameters over ad hoc diffusion networks where the nodes directly communicate with their neighbors is a challenging task. The problem complexity is significantly high under the lack of knowledge which parameters are shared and among which network nodes. In this paper, we propose an adaptive framework suitable for this task. It is abstractly formulated in the Bayesian and information-theoretic paradigms and, therefore, versatile and easily applicable to a relatively wide class of models. If the observation models belong to the exponential family and the same functional types of prior probability distributions are used for estimation of the shared parameters, the method reduces to an analytically tractable variational algorithm extended by a procedure that passes messages among network nodes. A simulation example demonstrates that the collaboration improves estimation performance of both the shared and strictly local parameters, compared with the noncollaborative scenario.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

5153-5163

Kód UT WoS článku

000407100700002

EID výsledku v databázi Scopus

2-s2.0-85023175924