| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0346948 |
| utime |
20240103193823.0 |
| mtime |
20100914235959.9 |
| title
(primary) (eng) |
Bayesian averaging of regressive models |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0346947 |
| ISBN |
978-615-5044-00-7 |
| title
|
Proceedings of the 11th International PhD Workshop on Systems and Control |
| page_num |
1-6 |
| publisher |
| place |
Veszprém, Maďarsko |
| name |
Faculty of Information Technology, University of Pannonia |
| year |
2010 |
|
|
| keyword |
bayesian modelling |
| keyword |
model averaging |
| keyword |
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*0101119 |
| name1 |
Jirsa |
| name2 |
Ladislav |
| 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. |
|
| author
|
| ARLID |
cav_un_auth*0234872 |
| name1 |
Pištěk |
| name2 |
Miroslav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
7D09008 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0261683 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
In the real world, it is often possible to model certain variables using several different regressive models. However, as the theoretical (mathematical, physical...) description of the real world is almost never perfect, there exists uncertainty about the true or best-fitting model. This paper deals with an issue of `mixing' information from multiple potentially true models, which run in parallel, to obtain a single outcome, taking the model uncertainty into account. While this issue has been addressed by many research papers in the past, most of them were developed for the static cases or for the state-space models. Here, we discuss an enhancement for a class of input-output regressive models. The described method allows to switch among models to reflect their modelling performance. If there is a single best model, the method quickly converges to it. |
| action |
| ARLID |
cav_un_auth*0263709 |
| name |
11th International PhD Workshop on Systems and Control a Young Generation Viewpoint |
| place |
Veszprém |
| dates |
01.09.2010-03.09.2010 |
| country |
HU |
|
| reportyear |
2011 |
| RIV |
JD |
| permalink |
http://hdl.handle.net/11104/0187843 |
| arlyear |
2010 |
| mrcbU63 |
cav_un_epca*0346947 Proceedings of the 11th International PhD Workshop on Systems and Control 978-615-5044-00-7 1 6 Sborník příspěvků 11. mezinárodního PhD workshopu o systémech a řízení Veszprém, Maďarsko Faculty of Information Technology, University of Pannonia 2010 |
|