Theory-based measure derives bounds for symmetric information in communication systems, suggesting new insights.
A non-parametric theory-based innovative symmetric information divergence measure for communication systems is proposed. This information measure belongs to the family of f-divergence. Furthermore, for the first time, we derive some bounds i.e. equalities and inequalities for this information divergence measure with well-known divergence measures, namely: Symmetric Chi-Square divergence Measure, Kumar & Johnson divergence and Bhattacharyya information measure, based on two distinct discrete probability distributions
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Choudhary et al. (2025) studied this question.
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