bibtype J - Journal Article
ARLID 0410587
utime 20240103182224.7
mtime 20060210235959.9
title (primary) (eng) Bayesian M-T clustering for reduced parameterisation of Markov chains used for non-linear adaptive elements
specification
page_count 8 s.
serial
ARLID cav_un_epca*0256218
ISSN 0005-1098
title Automatica
volume_id 37
volume 6 (2001)
page_num 1071-1078
publisher
name Elsevier
keyword Markov chain
keyword clustering
keyword Bayesian mixture estimation
author (primary)
ARLID cav_un_auth*0101219
name1 Valečková
name2 Markéta
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0212452
name1 Sutanto
name2 E. L.
country GB
COSATI 09I
cas_special
project
project_id 1999/12058
agency IST
country XE
project
project_id GA102/99/1564
agency GA ČR
ARLID cav_un_auth*0004444
research AV0Z1075907
abstract (eng) Markov chains are black box models ideal for describing stochastic digitised systems. Although the identification of their parameters can be a relatively easy task to perform, the dimensionality involved become undesirable large. This significant drawback can be overcome by exploiting smoothness of the underlying system. The paper present a novel hybrid off-line algorithm to locate areas which merit detailed model description. It comprises Bayesian parameter estimation and Mean tracking algorithm.
RIV BC
department AS
permalink http://hdl.handle.net/11104/0130676
ID_orig UTIA-B 20010056
arlyear 2001
mrcbU63 cav_un_epca*0256218 Automatica 0005-1098 1873-2836 Roč. 37 č. 6 2001 1071 1078 Elsevier