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 |
|
|
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 |
|
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 |
mrcbT16-e |
ENGINEERINGMULTIDISCIPLINARY|MATHEMATICSINTERDISCIPLINARYAPPLICATIONS|MECHANICS |
mrcbT16-f |
2.195 |
mrcbT16-g |
0.660 |
mrcbT16-h |
3.V |
mrcbT16-i |
0.01986 |
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 |
mrcbT16-E |
Q2 |
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 |
|