Článek v odborném periodiku

Raftery A., Kárný M., Ettler P.

Technometrics

2010

52-66

0040-1706

Ing. Miroslav Kárný, DrSc. (ÚTIA - Oddělení adaptivních systémů)

prediction, rolling mills, Bayesian Dynamic Averaging

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.