The central limit theorem shows how, when samples become large, the probability laws of large numbers and related facts are guaranteed to hold over wide domains. This thorough treatise on the subject, by an acknowledged expert, includes several topics not found in any other book, such as the treatment of VC combinatorics, the proofs of a bootstrap central limit theorem and of invariance principles. It also includes problems at the end of each chapter. The book will interest mathematicians working in probability, mathematical statisticians, and computer scientists working in computer learning theory.

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