ML p(r)ior | Beyond Countable Alphabets: An Extension of the Information-Spectrum Approach

Beyond Countable Alphabets: An Extension of the Information-Spectrum Approach

2016-02-01
A general approach is established for deriving one-shot performance bounds for information-theoretic problems on general alphabets beyond countable alphabets. It is mainly based on the quantization idea and a novel form of "likelihood ratio". As an example, one-shot lower and upper bounds for random number generation from correlated sources on general alphabets are derived.
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