DocumentCode :
1779868
Title :
Information geometry approach to parameter estimation in Markov chains
Author :
Hayashi, Mariko ; Watanabe, Shigetaka
Author_Institution :
Grad. Sch. of Math., Nagoya Univ., Nagoya, Japan
fYear :
2014
fDate :
June 29 2014-July 4 2014
Firstpage :
1091
Lastpage :
1095
Abstract :
We consider the parameter estimation of Markov chain when the unknown transition matrix belongs to an exponential family of transition matrices. Then, we show that the sample mean of the generator of the exponential family is an asymptotically efficient estimator. Further, we also define a curved exponential family of transition matrices. Using a transition matrix version of the Pythagorean theorem, we give an asymptotically efficient estimator for a curved exponential family.
Keywords :
Markov processes; information theory; matrix algebra; Markov chains; Pythagorean theorem; asymptotically efficient estimator; curved exponential family; information geometry approach; parameter estimation; unknown transition matrix; Educational institutions; Entropy; Generators; Information geometry; Information theory; Markov processes; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory (ISIT), 2014 IEEE International Symposium on
Conference_Location :
Honolulu, HI
Type :
conf
DOI :
10.1109/ISIT.2014.6875001
Filename :
6875001
Link To Document :
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