bibtype J - Journal Article
ARLID 0411034
utime 20240103182257.0
mtime 20060210235959.9
title (primary) (eng) Bayesian estimation of traffic lane state
specification
page_count 15 s.
serial
ARLID cav_un_epca*0256772
ISSN 0890-6327
title International Journal of Adaptive Control and Signal Processing
volume_id 17
volume 1 (2003)
page_num 51-65
publisher
name Wiley
keyword mixture models
keyword estimation
keyword Bayesian approach
author (primary)
ARLID cav_un_auth*0101167
name1 Nagy
name2 Ivan
institution UTIA-B
full_dept Department of Signal Processing
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*0101168
name1 Nedoma
name2 Petr
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0213007
name1 Voráčová
name2 Š.
country CZ
source
url http://library.utia.cas.cz/prace/20030021.ps
COSATI 09I
cas_special
project
project_id GA102/03/0049
agency GA ČR
ARLID cav_un_auth*0001805
project
project_id IBS1075351
agency GA AV ČR
ARLID cav_un_auth*0001804
research CEZ:AV0Z1075907
abstract (eng) The paper deals with modelling and estimation by a model described as a mixture of distributions. In this case, the exact application of the Bayes theory adopted is not feasible and its approximation is used. The general algorithm is specified for mixtures with components from exponential family. The theory is demonstrated on estimation of the basic relation between density and intensity of traffic flow in a single point of a vehicular communication and it can provides us with state classification.
RIV BB
department AS
permalink http://hdl.handle.net/11104/0131121
ID_orig UTIA-B 20030021
arlyear 2003
mrcbU63 cav_un_epca*0256772 International Journal of Adaptive Control and Signal Processing 0890-6327 1099-1115 Roč. 17 č. 1 2003 51 65 Wiley