Model Based Inference in the Life Sciences: A Primer on Evidence by David R. AndersonModel Based Inference in the Life Sciences: A Primer on Evidence by David R. Anderson

Model Based Inference in the Life Sciences: A Primer on Evidence

byDavid R. Anderson

Paperback | June 11, 2010

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This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.
Title:Model Based Inference in the Life Sciences: A Primer on EvidenceFormat:PaperbackDimensions:208 pages, 9.25 × 6.1 × 0.03 inPublished:June 11, 2010Publisher:Springer New YorkLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0387740732

ISBN - 13:9780387740737

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Table of Contents

Introduction--science hypotheses and science philosophy.- Data and models.- Information theory and entropy.- Quantifying the evidence about science hypotheses.- Multimodel inference.- Advanced topics.- Summary.

Editorial Reviews

From the reviews:".. The writing style is pragmatic and appropriate for someone without advanced statistical training. Readers looking to recommend a book on information-criteria-based modeling to colleagues who are not statisticians, or looking to locate such a book for their libraries are likely to be satisfied with this book. " (Biometrics, December 2008, Brief Reports by the Editor)"This . book provides an introduction to this approach of evidence-based inference. It is focused on advocating and teaching the approach. It includes some history and philosophy with the methods, and each chapter ends with exercises. . For those who are already familiar with model-based inference . it provides a more in-depth account of the information theoretical approach. For those who are new to model-based inference, it provides a good conceptual and technical introduction." (Glenn Suter, Integrated Environmental Assessment and Management, Vol. 5 (2), 2009)"Readership: Researchers and graduate students in ecology and other life sciences. This monograph expounds ideas that the author has developed over many years with Burnham. It is heavily example-based, and aimed at working scientists. Examples are predominately from ecological studies. . This is an interesting and challenging . book." (John H. Maindonald, International Statistical Review, Vol. 77 (3), 2009)".Presents an information-theoretic approach to statistical inference.Well motivated, clearly written, and thought provoking for its targeted readership. ." (The American Statistician, February 2010, Vol. 64, No. 1)