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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F10%3A00342595%21RIV11-MSM-67985556
rdf:type
skos:Concept n17:Vysledek
dcterms:description
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%22 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. 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%22 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.
dcterms:title
Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill
skos:prefLabel
Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill
skos:notation
RIV/67985556:_____/10:00342595!RIV11-MSM-67985556
n3:aktivita
n8:Z n8:P
n3:aktivity
P(1M0572), P(7D09008), Z(AV0Z10750506)
n3:cisloPeriodika
Number 1
n3:dodaniDat
n9:2011
n3:domaciTvurceVysledku
n4:6585256
n3:druhVysledku
n14:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
277000
n3:idVysledku
RIV/67985556:_____/10:00342595
n3:jazykVysledku
n18:eng
n3:klicovaSlova
prediction; rolling mills; Bayesian Dynamic Averaging
n3:klicoveSlovo
n10:rolling%20mills n10:Bayesian%20Dynamic%20Averaging n10:prediction
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[7B2870378042]
n3:nazevZdroje
Technometrics
n3:obor
n15:BC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n7:1M0572 n7:7D09008
n3:rokUplatneniVysledku
n9:2010
n3:svazekPeriodika
Volume 52
n3:tvurceVysledku
Kárný, Miroslav Ettler, P. Raftery, A. E.
n3:wos
000275920200006
n3:zamer
n16:AV0Z10750506
s:issn
0040-1706
s:numberOfPages
15