Diffusion estimation of mixture models with local and global parameters

0461646 - ÚTIA 2017 RIV ES eng C - Conference Paper (international conference)
Dedecius, Kamil - Sečkárová, Vladimíra
Diffusion estimation of mixture models with local and global parameters.
Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing. Palma de Mallorca, Španělsko: IEEE, 2016, s. 362-366. ISBN 978-1-4673-7802-4.
[2016 IEEE Statistical Signal Processing Workshop. Palma de Mallorca (ES), 26.06.2016-29.06.2016]
R&D Projects: GA ČR(CZ) GP14-06678P; GA ČR GA13-13502S
Institutional support: RVO:67985556
Keywords : diffusion estimation * distributed estimation * exponential family
Subject RIV: BD - Theory of Information
http://library.utia.cas.cz/separaty/2016/AS/dedecius-0461646.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0261345