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A New Lower Bound for Kullback-Leibler Divergence Based on Hammersley-Chapman-Robbins Bound

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  • Nishiyama, Tomohiro

Abstract

In this paper, we derive a useful lower bound for the Kullback-Leibler divergence (KL-divergence) based on the Hammersley-Chapman-Robbins bound (HCRB). The HCRB states that the variance of an estimator is bounded from below by the Chi-square divergence and the expectation value of the estimator. By using the relation between the KL-divergence and the Chi-square divergence, we show that the lower bound for the KL-divergence which only depends on the expectation value and the variance of a function we choose. This lower bound can also be derived from an information geometric approach. Furthermore, we show that the equality holds for the Bernoulli distributions and show that the inequality converges to the Cram\'{e}r-Rao bound when two distributions are very close. We also describe application examples and examples of numerical calculation.

Suggested Citation

  • Nishiyama, Tomohiro, 2019. "A New Lower Bound for Kullback-Leibler Divergence Based on Hammersley-Chapman-Robbins Bound," OSF Preprints wa98j, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:wa98j
    DOI: 10.31219/osf.io/wa98j
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    References listed on IDEAS

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    1. Masashi Sugiyama & Taiji Suzuki & Shinichi Nakajima & Hisashi Kashima & Paul Bünau & Motoaki Kawanabe, 2008. "Direct importance estimation for covariate shift adaptation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 699-746, December.
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    Cited by:

    1. Nishiyama, Tomohiro, 2019. "Monotonically Decreasing Sequence of Divergences," OSF Preprints wr2s6, Center for Open Science.

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