Tailored for those seeking a swift acquisition of knowledge in probability and statistics, this book serves as a concise and all-encompassing compendium that synthesizes many of the essential ideas prevalent in contemporary statistics. It is an ideal resource for students and scholars engaged in disciplines such as statistics, computer science, data mining, and machine learning.

The book transcends the scope of standard introductory texts on mathematical statistics, presenting a wider array of topics. It delves into sophisticated subjects such as nonparametric curve estimation, bootstrapping techniques, and classification methods, which are typically explored in more advanced or follow-up courses. The reader is expected to possess a foundational knowledge of calculus and a modest familiarity with linear algebra. Importantly, no background in probability and statistics is assumed. This versatile text is applicable to learners at both the advanced undergraduate and graduate educational levels.

Larry Wasserman, a distinguished Professor of Statistics at Carnegie Mellon University, is also a member of the prestigious Center for Automated Learning and Discovery within the School of Computer Science. His scholarly pursuits encompass nonparametric inference, asymptotic theory, causal analysis, and the application of statistical methods in astrophysics, bioinformatics, and genetics. Recognized for his contributions, he was awarded the Committee of Presidents of Statistical Societies Presidents’ Award in 1999 and the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics in 2002. Dr. Wasserman serves as an Associate Editor for esteemed journals, including 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 Mathematical Statistics.