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Monotonic Convergence in an Information-Theoretic Law of Small Numbers

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1 Author(s)
Yaming Yu ; Dept. of Stat., Univ. of California, Irvine, CA, USA

An "entropy increasing to the maximum" result analogous to the entropic central limit theorem (Barron 1986; Artstein 2004) is obtained in the discrete setting. This involves the thinning operation and a Poisson limit. Monotonic convergence in relative entropy is established for general discrete distributions, while monotonic increase of Shannon entropy is proved for the special class of ultra-log-concave distributions. Overall we extend the parallel between the information-theoretic central limit theorem and law of small numbers explored by Kontoyiannis (2005) and HarremoEumls (2007, 2008, 2009). Ingredients in the proofs include convexity, majorization, and stochastic orders.

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Information Theory, IEEE Transactions on  (Volume:55 ,  Issue: 12 )