期刊
IEEE TRANSACTIONS ON INFORMATION THEORY
卷 52, 期 8, 页码 3456-3475出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2006.878182
关键词
block sorting; Burrows-Wheeler transform; context tree weighting method; divergence estimation; information divergence; Markov sources; universal methods
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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