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International Edition

Intl. Ed.

All Of Statistics A Concise Course In Statistical Inference (Pb 2004)

Intl. Ed.

All Of Statistics A Concise Course In Statistical Inference (Pb 2004) Paperback - 2010

by Wasserman L

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International Edition. Brand New. Softcover,International Edition,Cover & ISBN may be different. But Contents are same as US Edition.Printed in English Language, we do not ship APO, PO Box Address.
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Details

  • Title All Of Statistics A Concise Course In Statistical Inference (Pb 2004)
  • Author Wasserman L
  • Binding Paperback
  • Edition International Edition
  • Condition New
  • Pages 442
  • Volumes 1
  • Language ENG
  • Publisher Springer, Pittsburgh, Pennsylvania
  • Publication date 2010-12-01
  • Bookseller's Inventory # cb-s-001-6966
  • ISBN 9781441923226 / 1441923225
  • Weight 1.43 lbs (0.65 kg)
  • Dimensions 9.21 x 6.14 x 0.94 in (23.39 x 15.60 x 2.39 cm)
  • Category Mathematics
  • Dewey Decimal Code 005.55
  • Quantity available 2

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Reader reviews for All Of Statistics A Concise Course In Statistical Inference (Pb 2004)

From the publisher

This book surveys a broad range of topics in probability and mathematical statistics. It provides the statistical background that a computer scientist needs to work in the area of machine learning.

From the rear cover

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning.

This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

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.

About the author

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.
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