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Universal Divergence Estimation for Finite-Alphabet Sources

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3 Author(s)
Haixiao Cai ; Dept. of Electr. Eng., Princeton Univ., NJ ; Kulkarni, S.R. ; Verdu, S.

This paper studies universal estimation of divergence from the realizations of two unknown finite-alphabet sources. Two algorithms that borrow techniques from data compression are presented. The first divergence estimator applies the Burrows-Wheeler block sorting transform to the concatenation of the two realizations; consistency of this estimator is shown for all finite-memory sources. The second divergence estimator is based on the Context Tree Weighting method; consistency is shown for all sources whose memory length does not exceed a known bound. Experimental results show that both algorithms perform similarly and outperform string-matching and plug-in methods

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Information Theory, IEEE Transactions on  (Volume:52 ,  Issue: 8 )