モデル選択と多重モデル推測(第2版)<br>Model Selection and Multi-Model Inference : A Practical Information-Theoretic Approach (New ed. 2004. XXVI, 488 p. w. 31 figs. 24,5 cm)

モデル選択と多重モデル推測(第2版)
Model Selection and Multi-Model Inference : A Practical Information-Theoretic Approach (New ed. 2004. XXVI, 488 p. w. 31 figs. 24,5 cm)

  • ただいまウェブストアではご注文を受け付けておりません。 ⇒古書を探す
  • 製本 Hardcover:ハードカバー版/ページ数 488 p.
  • 商品コード 9780387953649

基本説明

Focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference). The book invites increased attention on a priori science hypotheses and modeling.

Full Description

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. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

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