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The paper examines relationships between the conditional Shannon entropy and the expectation of &#x2113;&#x03B1;-norm for joint probability distributions. More precisely, we investigate the sharp bounds of the expectation of &#x2113;&#x03B1;-norm with a fixed conditional Shannon entropy, and vice versa. As applications of the results, we derive the sharp bounds between the conditional Shannon entropy and several information measures which are determined by the expectation of &#x2113;&#x03B1;-norm, e.g., Arimoto's conditional Re&#x0301;nyi entropy and the conditional R-norm information. Moreover, we apply these results to discrete memoryless channels under a uniform input distribution. Then, sharp bounds are obtained for Gallager's reliability functions E0 with a fixed mutual information under a uniform input distribution.

Relations between conditional Shannon entropy and expectation of ℓα-norm