DocumentCode :
995776
Title :
A Posteriori Probability Distances Between Finite-Alphabet Hidden Markov Models
Author :
Xie, Li ; Ugrinovskii, Valery A. ; Petersen, Ian R.
Author_Institution :
Sch. of Inf. Technol. & Electr. Eng., Univ. of New South Wales at the Australian Defence Force Acad., Canberra, ACT
Volume :
53
Issue :
2
fYear :
2007
Firstpage :
783
Lastpage :
793
Abstract :
In this correspondence, we consider a probability distance problem for a class of hidden Markov models (HMMs). The notion of conditional relative entropy between conditional probability measures is introduced as an a posteriori probability distance which can be used to measure the discrepancy between hidden Markov models when a realized observation sequence is observed. Using a measure change technique, we derive a representation for conditional relative entropy in terms of the parameters of the HMMs and conditional expectations given measurements. With this representation, we show that this distance can be calculated using an information state approach
Keywords :
entropy; hidden Markov models; probability; aposteriori probability distances; conditional relative entropy; finite-alphabet HMM; hidden Markov models; information state method; measure change technique; Automatic control; Control systems; Entropy; Hidden Markov models; Interchannel interference; Multiaccess communication; Power control; Routing; Spread spectrum communication; Symmetric matrices; A posteriori probability distances; conditional relative entropy; finite-alphabet hidden Markov models; information state; measure change;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2006.887076
Filename :
4069166
Link To Document :
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