Abstract -A new class of information-theoretic divergence measures based on the Shannon entropy is introduced. Unlike the well-known Kullback divergences, the ...
Missing: Tinfinity. | Show results with:Tinfinity.
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May 16, 2022 · Basic properties of an f-divergence are its non-negativity, convexity in the pair of probability measures, and the satisfiability of data- ...
Missing: Tinfinity. | Show results with:Tinfinity.
A novel class of information-theoretic divergence measures based on the Shannon entropy is introduced, which do not require the condition of absolute ...
Missing: Tinfinity. | Show results with:Tinfinity.
Jan 2, 2012 · These measures are bounded, symmetric and positive semi-definite and do not require absolute continuity. In the asymptotic limit, BBD measure ...
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Abstract - Three measures of divergence between vectors in a convex set of an n-dimensional real vector space have been defined in terms of certain types of ...
Connexin genes are expressed in a cell type-specific manner with overlapping specificity. Based on analyses of amino acid sequences and labeling of membrane- ...
Some widely used and well known divergences like Kullback-Leibler divergence or Jeffreys divergence are based on entropy concepts. In this study, we compare ...
The first criterion is defined to ensure a correct discretization of the mean field, where the mean field is the same field resolved with RANS-based grid ...
The measure is based on high frequency data on commodity terms of trade volatility since 1980. The source of the data is Cavalcanti, Mohaddes, and Raisi ...
It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it always has a finite value. ...
Missing: Tinfinity. | Show results with:Tinfinity.