Universal Divergence Estimation for Finite-Alphabet Sources
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 th...
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Veröffentlicht in: | IEEE transactions on information theory 2006-08, Vol.52 (8), p.3456-3475 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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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ISSN: | 0018-9448 1557-9654 |
DOI: | 10.1109/TIT.2006.878182 |