  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.
