All of statistics

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This book is designed for those eager to quickly grasp probability and statistics. It consolidates key concepts in modern statistics, making it ideal for students and researchers in statistics, computer science, data mining, and machine learning. Unlike typical introductory texts, it covers a broader range of topics, including modern subjects like nonparametric curve estimation, bootstrapping, and classification, which are often reserved for advanced courses. A background in calculus and some linear algebra is assumed, but no prior knowledge of probability and statistics is necessary. The content is suitable for advanced undergraduate and graduate students. The author, Larry Wasserman, is a Professor of Statistics at Carnegie Mellon University and a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research focuses on nonparametric inference, asymptotic theory, causality, and applications in fields such as astrophysics, bioinformatics, and genetics. Wasserman has received several prestigious awards, including the Committee of Presidents of Statistical Societies Presidents' Award and the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics. He serves as an Associate Editor for The Journal of the American Statistical Association and The Annals of Statistics and is a fellow of both the American Statistical Association and the Institute of M