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• W2510365931 abstract "The state-of-art methods for distributed estimation of mixtures assume the existence of a common mixture model. In many practical situations, this assumption may be too restrictive, as a subset of parameters may be purely local, e.g., if the numbers of observable components differ across the network. To reflect this issue, we propose a new online Bayesian method for simultaneous estimation of local parameters, and diffusion estimation of global parameters. The algorithm consists of two steps. First, the nodes perform local estimation from own observations by means of factorized prior/posterior distributions. Second, a diffusion optimization step is used to merge the nodes' global parameters estimates. A simulation example demonstrates improved performance in estimation of both parameters sets." @default.
• W2510365931 created "2016-09-16" @default.
• W2510365931 creator A5044323717 @default.
• W2510365931 creator A5053280151 @default.
• W2510365931 date "2016-06-01" @default.
• W2510365931 modified "2023-09-26" @default.
• W2510365931 title "Diffusion estimation of mixture models with local and global parameters" @default.
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• W2510365931 doi "https://doi.org/10.1109/ssp.2016.7551775" @default.
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