Data Processing Inequality
The data processing inequality is a concept that can be expressed concisely as post-processing cannot increase information.
Fano's inequality gives a lower bound for the probability of error for m-ary hypothesis testing problem.
Variation formulae for information theory quantities in recent years become important for both theoretical as well as practical reasons. There are numerous ways of the application of variational formulae, such as:
• Information radius interpretation of capacity,
• Pinsker's inequality,
• Continuity of entropy.
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