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Bibliography

Journal Article

Sequential Estimation of Mixtures in Diffusion Networks

Dedecius Kamil, Reichl Jan, Djurić P. M.

: IEEE Signal Processing Letters vol.22, 2 (2015), p. 197-201

: GP14-06678P, GA ČR

: distributed estimation, mixture models, bayesian inference

: 10.1109/LSP.2014.2353652

: http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431479.pdf

(eng): 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.

: BB

2019-01-07 08:39