Sequential estimation of mixtures in diffusion networks

Kamil Dedecius; Jan Reichl; Petar M. Djuric

doi:10.1109/LSP.2014.2353652

Sequential estimation of mixtures in diffusion networks

Kamil Dedecius
, Jan Reichl
, Petar M. Djuric

Electrical Engineering

Czech Academy of Sciences

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

The letter studies the problem of sequential estimation of mixtures in diffusion networks whose nodes communicate only with their adjacent neighbors. The adopted quasi-Bayesian approach yields a probabilistically consistent and computationally non-intensive and fast method, applicable to a wide class of mixture models with unknown component parameters and weights. Moreover, if conjugate priors are used for inferring the component parameters, the solution attains a closed analytic form.

Original language	English
Article number	6892971
Pages (from-to)	197-201
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	22
Issue number	2
DOIs	https://doi.org/10.1109/LSP.2014.2353652
State	Published - Feb 1 2015

Keywords

Diffusion
distributed parameter estimation
sensor networks
sequential mixture estimation

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10.1109/LSP.2014.2353652

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title = "Sequential estimation of mixtures in diffusion networks",

abstract = "The letter studies the problem of sequential estimation of mixtures in diffusion networks whose nodes communicate only with their adjacent neighbors. The adopted quasi-Bayesian approach yields a probabilistically consistent and computationally non-intensive and fast method, applicable to a wide class of mixture models with unknown component parameters and weights. Moreover, if conjugate priors are used for inferring the component parameters, the solution attains a closed analytic form.",

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AB - The letter studies the problem of sequential estimation of mixtures in diffusion networks whose nodes communicate only with their adjacent neighbors. The adopted quasi-Bayesian approach yields a probabilistically consistent and computationally non-intensive and fast method, applicable to a wide class of mixture models with unknown component parameters and weights. Moreover, if conjugate priors are used for inferring the component parameters, the solution attains a closed analytic form.

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KW - distributed parameter estimation

KW - sensor networks

KW - sequential mixture estimation

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Sequential estimation of mixtures in diffusion networks

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Keywords

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