商品描述
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.
商品描述(中文翻譯)
字面上來看，書名「所有統計」是一種誇張。但從精神上來說，這個書名是恰當的，因為這本書涵蓋的主題範圍比典型的數學統計入門書要廣泛得多。本書適合那些希望快速學習機率和統計的人。它適合計算機科學、數學、統計及相關學科的研究生或高年級本科生。這本書包括現代主題，如非參數曲線估計、重抽樣（bootstrapping）和分類，這些主題通常被 relegated 到後續課程中。讀者應該具備微積分和一些線性代數的知識，無需具備先前的機率和統計知識。統計、資料探勘和機器學習都與收集和分析數據有關。曾幾何時，統計研究是在統計系進行的，而資料探勘和機器學習研究則是在計算機科學系進行的。統計學家認為計算機科學家在重新發明輪子，而計算機科學家則認為統計理論不適用於他們的問題。這一切正在改變。統計學家現在認識到計算機科學家正在做出新穎的貢獻，而計算機科學家也開始認識到統計理論和方法的普遍性。巧妙的資料探勘算法比統計學家曾經想像的更具可擴展性。正式的統計理論比計算機科學家所意識到的更為普遍。
作者簡介
Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.
作者簡介(中文翻譯)
拉里·瓦瑟曼（Larry Wasserman）是卡內基梅隆大學（Carnegie Mellon University）統計學教授。他同時也是計算機科學學院自動學習與發現中心的成員。他的研究領域包括非參數推斷、漸近理論、因果關係，以及在天體物理學、生物資訊學和遺傳學中的應用。他是1999年統計學會會長委員會（Committee of Presidents of Statistical Societies）會長獎的得主，以及2002年蒙特利爾數學研究中心（Centre de recherches mathematiques de Montreal）-加拿大統計學會（Statistical Society of Canada）統計獎的得主。他是《美國統計協會期刊》（The Journal of the American Statistical Association）和《統計年鑑》（The Annals of Statistics）的副編輯。他是美國統計協會（American Statistical Association）和數學統計學會（Institute of Mathematical Statistics）的會士。
