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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Sequential estimation and diffusion of information over networks: A Bayesian approach with exponential family of distributions
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics

Project
<a href="/en/project/GP14-06678P" target="_blank" >GP14-06678P: Distributed dynamic estimation in diffusion networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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