Taken literally, the title "All of Statistics" is an exaggeration. But
in spirit, the title is apt, as the book does cover a much broader range
of topics than a typical introductory book on mathematical statistics.
This book is for people who want to learn probability and statistics
quickly. It is suitable for graduate or advanced undergraduate students
in computer science, mathematics, statistics, and related disciplines.
The book includes modern topics like nonparametric curve estimation,
bootstrapping, and clas- sification, topics that are usually relegated
to follow-up courses. The reader is presumed to know calculus and a
little linear algebra. No previous knowledge of probability and
statistics is required. Statistics, data mining, and machine learning
are all concerned with collecting and analyzing data. For some time,
statistics research was con- ducted in statistics departments while data
mining and machine learning re- search was conducted in computer science
departments. Statisticians thought that computer scientists were
reinventing the wheel. Computer scientists thought that statistical
theory didn't apply to their problems. Things are changing.
Statisticians now recognize that computer scientists are making novel
contributions while computer scientists now recognize the generality of
statistical theory and methodology. Clever data mining algo- rithms are
more scalable than statisticians ever thought possible. Formal sta-
tistical theory is more pervasive than computer scientists had realized.
