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Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach

Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach

Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach Hardback - 2002

by Kenneth P. Burnham

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Hardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and
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Details

  • Title Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach
  • Author Kenneth P. Burnham
  • Binding Hardback
  • Edition 2nd
  • Condition New
  • Pages 488
  • Volumes 1
  • Language ENG
  • Publisher Springer, New York, America.
  • Publication date 2002-07-12
  • Illustrated Yes
  • Features Bibliography, Glossary, Illustrated, Index, Table of Contents
  • Bookseller's Inventory # ria9780387953649_inp
  • ISBN 9780387953649 / 0387953647
  • Weight 1.89 lbs (0.86 kg)
  • Dimensions 9.44 x 6.34 x 1.12 in (23.98 x 16.10 x 2.84 cm)
  • Themes
    • Topical: Ecology
  • Category Science
  • Library of Congress subjects Biology - Mathematical models, Mathematical statistics
  • Library of Congress Catalogue Number 2001057677
  • Dewey Decimal Code 570.151
  • Quantity available 701

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Reader reviews for Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach

From the publisher

Statisticians and applied scientists often must select a model to fit empirical data. This book introduces researchers and graduate students in many areas to an information criterion approach, first introduced by Hirotugu Akaike in 1973. The book will be of general interest, but the emphasis is on applications to the biological sciences.

First line

This book is about making valid inferences from scientific data when a meaningful analysis depends on a model of the information in the data.
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