All of Statistics
A Concise Course in Statistical Inference
Nonfiction, Science & Nature, Mathematics, Counting & Numeration, Statistics
| Author: | Larry Wasserman | ISBN: | 9780387217369 |
| Publisher: | Springer New York | Publication: | December 11, 2013 |
| Imprint: | Springer | Language: | English |
| Author: | Larry Wasserman |
| ISBN: | 9780387217369 |
| Publisher: | Springer New York |
| Publication: | December 11, 2013 |
| Imprint: | Springer |
| Language: | English |
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 non-parametric curve estimation, bootstrapping, and classification, 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 analysing data.
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 non-parametric curve estimation, bootstrapping, and classification, 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 analysing data.
