bibtype V - Research Report
ARLID 0453623
utime 20240103211549.0
mtime 20160215235959.9
title (primary) (eng) Diffusion MCMC for Mixture Estimation
publisher
place Praha
pub_time 2016
specification
page_count 11 s.
media_type P
edition
name Research Report
volume_id 2354
keyword Mixture
keyword mixture estimation
keyword MCMC
author (primary)
ARLID cav_un_auth*0306030
name1 Reichl
name2 Jan
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0242543
name1 Dedecius
name2 Kamil
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2016/AS/dedecius-0453623.pdf
cas_special
project
project_id GP14-06678P
agency GA ČR
country CZ
ARLID cav_un_auth*0303543
abstract (eng) 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.
reportyear 2016
RIV BB
num_of_auth 2
mrcbC52 4 O 4o 20231122141423.4
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0257060
confidential S
arlyear 2016
mrcbTft \nSoubory v repozitáři: 0453623.pdf
mrcbU10 2016
mrcbU10 Praha