Generalized Symmetric Divergence Measures and the Probability of Error

Details Generalized Symmetric Divergence Measures and the Probability of Error Generalized Symmetric Divergence Measures and the Probability of Error

2011-03-27

arxiv.org/abs/1103.5218v2

Computer Science

Information Theory

Scientific paper

There are three classical divergence measures exist in the literature on information theory and statistics. These are namely, Jeffryes-Kullback-Leiber J-divergence. Sibson-Burbea-Rao Jensen-Shannon divegernce and Taneja Arithmetic-Geometric divergence. These three measures bear an interesting relationship among each other. The divergence measures like Hellinger discrimination, symmetric chi-square divergence, and triangular discrimination are also known in the literature. In this paper, we have considered generalized symmetric divergence measures having the measures given above as particular cases. Bounds on the probability of error are obtained in terms of generalized symmetric divergence measures. Study of bounds on probability of error is extended for the difference of divergence measures.

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