DocumentCode :
3471457
Title :
Maximum entropy identification and min-max optimal prediction
Author :
Shankwitz, Craig ; Georgiou, Tryphon T.
Author_Institution :
Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN, USA
fYear :
1991
fDate :
11-13 Dec 1991
Firstpage :
617
Abstract :
The authors consider the problem of worst case prediction of stationary discrete time stochastic processes. For certain classes of models and predictor, there is a uniformly optimal solution to the prediction problem. This solution is unique, and the uniform optimality is related to the principle of maximum entropy. An example is provided for which the min-max solution is not equal to the max-min solution
Keywords :
entropy; filtering and prediction theory; identification; stochastic processes; max-min solution; maximum entropy identification; min-max optimal prediction; stationary discrete time stochastic processes; uniformly optimal solution; worst case prediction; Cost function; Distribution functions; Entropy; Power system modeling; Predictive models; Signal processing; Stochastic processes; White noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
Type :
conf
DOI :
10.1109/CDC.1991.261383
Filename :
261383
Link To Document :
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