| bibtype |
J -
Journal Article
|
| ARLID |
0342595 |
| utime |
20240103193448.3 |
| mtime |
20100521235959.9 |
| SCOPUS |
77949408057 |
| WOS |
000275920200006 |
| DOI |
10.1198/TECH.2009.08104 |
| title
(primary) (eng) |
Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0255201 |
| ISSN |
0040-1706 |
| title
|
Technometrics |
| page_num |
52-66 |
|
| keyword |
prediction |
| keyword |
rolling mills |
| keyword |
Bayesian Dynamic Averaging |
| author
(primary) |
| ARLID |
cav_un_auth*0237107 |
| name1 |
Raftery |
| name2 |
A. E. |
| country |
US |
|
| author
|
| ARLID |
cav_un_auth*0101124 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0212695 |
| name1 |
Ettler |
| name2 |
P. |
| country |
CZ |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0001814 |
| project_id |
1M0572 |
| agency |
GA MŠk |
|
| project |
| ARLID |
cav_un_auth*0261683 |
| project_id |
7D09008 |
| agency |
GA MŠk |
| country |
CZ |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called Dynamic Model Averaging (DMA) in which a state space model for the parameters of each model is combined with a Markov chain model for the correct model. This allows the "correct" model to vary over time. The state space and Markov chain models are both specied in terms of forgetting, leading to a highly parsimonious representation. As a special case, when the model and parameters do not change, DMA is a recursive implementation of standard Bayesian model averaging, which we call recursive model averaging (RMA). The method is applied to the problem of predicting the output strip thickness for a cold rolling mill, where the output is measured with a time delay. |
| RIV |
BC |
| reportyear |
2011 |
| mrcbC52 |
4 A 4a 20231122134017.2 |
| permalink |
http://hdl.handle.net/11104/0185291 |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-f |
1.985 |
| mrcbT16-g |
0.206 |
| mrcbT16-h |
>10.0 |
| mrcbT16-i |
0.00558 |
| mrcbT16-j |
1.424 |
| mrcbT16-k |
4488 |
| mrcbT16-l |
34 |
| mrcbT16-s |
1.500 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
78.067 |
| mrcbT16-C |
75.909 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2010 |
| mrcbTft |
\nSoubory v repozitáři: karny-0342595.pdf |
| mrcbU14 |
77949408057 SCOPUS |
| mrcbU34 |
000275920200006 WOS |
| mrcbU63 |
cav_un_epca*0255201 Technometrics 0040-1706 1537-2723 Volume 52 Number 1 2010 52 66 |
|