| bibtype |
J -
Journal Article
|
| ARLID |
0370444 |
| utime |
20240103200153.7 |
| mtime |
20120109235959.9 |
| WOS |
000301342800002 |
| DOI |
10.1080/03610918.2011.598992 |
| title
(primary) (eng) |
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0256434 |
| ISSN |
0361-0918 |
| title
|
Communications in Statistics - Simulation and Computation |
| volume_id |
41 |
| volume |
5 (2012) |
| page_num |
582-589 |
| publisher |
|
|
| keyword |
Bayesian methods |
| keyword |
Particle filters |
| keyword |
Recursive estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0242543 |
| name1 |
Dedecius |
| name2 |
Kamil |
| 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 Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0228606 |
| name1 |
Hofman |
| name2 |
Radek |
| 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 |
|
| cas_special |
| project |
| project_id |
VG20102013018 |
| agency |
GA MV |
| ARLID |
cav_un_auth*0265869 |
|
| project |
| project_id |
GA102/08/0567 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239566 |
|
| project |
| project_id |
SGS 10/099/OHK3/1T/16 |
| agency |
ČVUT |
| country |
CZ |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
The authors are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. They propose a linear regression model within Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixture, where the weights of components are tuned with a particle filter. The mixture reflects a priori given hypotheses on different scenarios of (expected) parameters' evolution. |
| reportyear |
2012 |
| RIV |
BB |
| num_of_auth |
2 |
| permalink |
http://hdl.handle.net/11104/0204246 |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-f |
0.515 |
| mrcbT16-g |
0.065 |
| mrcbT16-h |
9.1 |
| mrcbT16-i |
0.00359 |
| mrcbT16-j |
0.332 |
| mrcbT16-k |
838 |
| mrcbT16-l |
139 |
| mrcbT16-s |
0.425 |
| mrcbT16-4 |
Q3 |
| mrcbT16-B |
10.019 |
| mrcbT16-C |
4.701 |
| mrcbT16-D |
Q4 |
| mrcbT16-E |
Q4 |
| arlyear |
2012 |
| mrcbU34 |
000301342800002 WOS |
| mrcbU63 |
cav_un_epca*0256434 Communications in Statistics - Simulation and Computation 0361-0918 1532-4141 Roč. 41 č. 5 2012 582 589 Taylor & Francis |
|