Sequential estimation and diffusion of information over networks: A Bayesian approach with exponential family of distributions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00467560" target="_blank" >RIV/67985556:_____/17:00467560 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TSP.2016.2641380" target="_blank" >http://dx.doi.org/10.1109/TSP.2016.2641380</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2016.2641380" target="_blank" >10.1109/TSP.2016.2641380</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential estimation and diffusion of information over networks: A Bayesian approach with exponential family of distributions

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Sequential estimation and diffusion of information over networks: A Bayesian approach with exponential family of distributions

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GP14-06678P" target="_blank" >GP14-06678P: Distribuované dynamické odhadování v difuzních sítích</a><br>

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

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1795-1809

Kód UT WoS článku

000395484200012

EID výsledku v databázi Scopus

2-s2.0-85014905526