0453623 - ÚTIA 2016 CZ eng V - Výzkumná zpráva
Reichl, Jan - Dedecius, Kamil
Diffusion MCMC for Mixture Estimation.
Praha, 2016. 11 s. Research Report, 2354.
Grant CEP: GA ČR(CZ) GP14-06678P
Institucionální podpora: RVO:67985556
Klíčová slova: Mixture * mixture estimation * MCMC
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
http://library.utia.cas.cz/separaty/2016/AS/dedecius-0453623.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0257060