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Given training sequences generated by two distinct, but unknown, distributions on a common alphabet, we study the problem of determining whether a third sequence was generated according to the first or second distribution. To model sources such as natural language, for which the underlying distributions are difficult to learn from realistic amounts of data, we allow the alphabet size to grow and therefore...
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