bibtype J - Journal Article
ARLID 0392950
utime 20240103202624.5
mtime 20131111235959.9
WOS 000328806000012
DOI 10.1016/j.apm.2013.05.038
title (primary) (eng) Mixture estimation with state-space components and Markov model of switching
specification
page_count 21 s.
media_type P
serial
ARLID cav_un_epca*0252056
ISSN 0307-904X
title Applied Mathematical Modelling
volume_id 37
volume 24 (2013)
page_num 9970-9984
publisher
name Elsevier
keyword probabilistic dynamic mixtures,
keyword probability density function
keyword state-space models
keyword recursive mixture estimation
keyword Bayesian dynamic decision making under uncertainty
keyword Kerridge inaccuracy
author (primary)
ARLID cav_un_auth*0101167
name1 Nagy
name2 Ivan
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
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*0108105
name1 Suzdaleva
name2 Evgenia
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
institution UTIA-B
full_dept Department of Signal Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2013/AS/nagy-mixture estimation with state-space components and markov model of switching.pdf
cas_special
project
project_id TA01030123
agency GA TA ČR
ARLID cav_un_auth*0271776
abstract (eng) The paper proposes a recursive algorithm for estimation of mixtures with state-space components and a dynamic model of switching. Bayesian methodology is adopted. The main features of the presented approach are: (i) recursiveness that enables a real-time performance of the algorithm; (ii) one-pass elaboration of the data sample; (iii) dynamic nature of the model of switching active components; (iv) orientation at explicit solutions with exploitation of numerical procedures only in those parts which cannot be computed analytically; (v) systematic approach to the Bayesian mixture estimation theory.
reportyear 2014
RIV BC
num_of_auth 2
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0221975
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mrcbT16-j 0.632
mrcbT16-k 6174
mrcbT16-l 773
mrcbT16-s 1.074
mrcbT16-4 Q1
mrcbT16-B 55.924
mrcbT16-C 88.432
mrcbT16-D Q2
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arlyear 2013
mrcbU34 000328806000012 WOS
mrcbU63 cav_un_epca*0252056 Applied Mathematical Modelling 0307-904X 1872-8480 Roč. 37 č. 24 2013 9970 9984 Elsevier