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