. "1"^^ . "Volume 52" . "RIV/67985556:_____/10:00342595!RIV11-MSM-67985556" . "Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill" . . . . . "RIV/67985556:_____/10:00342595" . "15"^^ . . "[7B2870378042]" . . . "K\u00E1rn\u00FD, Miroslav" . . . . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill" . . . . . "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."@en . "Number 1" . "Technometrics" . . . "Ettler, P." . "3"^^ . "277000" . "prediction; rolling mills; Bayesian Dynamic Averaging"@en . "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." . "Raftery, A. E." . "Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill"@en . "000275920200006" . "0040-1706" . "P(1M0572), P(7D09008), Z(AV0Z10750506)" . . "Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill"@en .