Description

Book Synopsis
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 the selection of a good approximating model that best represents the inference supported by the data.

Table of Contents
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary

Model Selection and Multimodel Inference A

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    A Hardback by Kenneth P. Burnham, David R. Anderson

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      View other formats and editions of Model Selection and Multimodel Inference A by Kenneth P. Burnham

      Publisher: Springer New York
      Publication Date: 7/12/2002 12:00:00 AM
      ISBN13: 9780387953649, 978-0387953649
      ISBN10: 0387953647
      Also in:
      Mathematics Probability and statistics Science & Nature Ecological science, the Biosphere

      Description

      Book Synopsis
      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 the selection of a good approximating model that best represents the inference supported by the data.

      Table of Contents
      Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary

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