0453623 - ÚTIA 2016 CZ eng V - Research Report
Reichl, Jan - Dedecius, Kamil
Diffusion MCMC for Mixture Estimation.
Praha, 2016. 11 s. Research Report, 2354.
R&D Projects: GA ČR(CZ) GP14-06678P
Institutional support: RVO:67985556
Keywords : Mixture * mixture estimation * MCMC
Subject RIV: BB - Applied Statistics, Operational Research
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.
Permanent Link: http://hdl.handle.net/11104/0257060