Original title: Diffusion MCMC for Mixture Estimation
Authors: Reichl, Jan ; Dedecius, Kamil
Document type: Research reports
Year: 2016
Language: eng
Series: Research Report, volume: 2354
Abstract: Distributed inference of parameters of mixture models by a network of cooperating nodes (sensors) with computational and communication capabilities still represents a challenging task. In the last decade, several methods were proposed to solve this issue, predominantly formulated within the expectation-maximization framework and with the assumption of mixture components normality. The present paper adopts the Bayesian approach to inference of general (non-normal) mixtures via the Markov chain Monte Carlo simulation from the parameter posterior distribution. By collaborative tuning of node chains, the method allows reliable estimation even at nodes with significantly worse observational conditions, where the components may tend to merge due to high variances. The method runs in the diffusion networks, where the nodes communicate only with their adjacent neighbors within 1 hop distance.
Keywords: MCMC; Mixture; mixture estimation
Project no.: GP14-06678P (CEP)
Funding provider: GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2016/AS/dedecius-0453623.pdf
Original record: http://hdl.handle.net/11104/0257060

Permalink: http://www.nusl.cz/ntk/nusl-202695


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