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...

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Hauptverfasser:	Raftery, Adrian E, Karny, Miroslav, Andrysek, Josef, Ettler, Pavel
Format:	Report
Sprache:	eng
Schlagworte:	BAYESIAN MODEL AVERAGING DMA(DYNAMIC MODEL AVERAGING) MARKOV CHAIN MODELS MATHEMATICAL MODELS MONTE CARLO METHOD ONLINE PREDICTIONS PARAMETERS PREDICTIONS STATE SPACE MODELS Statistics and Probability UNCERTAINTY
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