Title :
Universal, nonlinear, mean-square prediction of Markov processes
Author :
Modha, Dharmendra S. ; Masry, Elias
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
Abstract :
We estimate the best, nonlinear, mean-square predictor for a Markov process from an observed, finite realization of the process when the true Markov order is unknown. In particular, we propose an universal minimum complexity estimator, which does not know the true Markov order, and yet delivers the same statistical performance as that delivered by a minimum complexity estimator, which knows the true Markov order
Keywords :
Markov processes; approximation theory; estimation theory; prediction theory; statistical analysis; Markov order; Markov processes; mean-square prediction; statistical performance; universal minimum complexity estimator; universal nonlinear prediction; Approximation error; Drives; Estimation error; Indium phosphide; Markov processes; Neural networks;
Conference_Titel :
Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on
Conference_Location :
Whistler, BC
Print_ISBN :
0-7803-2453-6
DOI :
10.1109/ISIT.1995.535774
