Research report

Raftery A., Kárný M., Andrýsek J., Ettler P.

Technical Report of the University of Washington

525

University of Washington

Seattle

2007

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

Bayesian averaging, Multiple models, Prediction

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. 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.