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

Model Based Inference in the Life Sciences: A Primer on Evidence
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Field name Details
Dewey Class 577
Title Model Based Inference in the Life Sciences: A Primer on Evidence (EB) / by David R. Anderson.
Author Anderson, David R.
Other name(s) SpringerLink (Online service)
Publication New York, NY : Springer , 2008.
Physical Details XXIV, 184 pages : 8 illus. : online resource.
ISBN 9780387740751
Summary Note The abstract concept of "information" can be quantified and this has led to many important advances in the analysis of data in the empirical sciences. This text focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The fundamental science question relates to the empirical evidence for hypotheses in this setâa formal strength of evidence. Kullback-Leibler information is the information lost when a model is used to approximate full reality. Hirotugu Akaike found a link between K-L information (a cornerstone of information theory) and the maximized log-likelihood (a cornerstone of mathematical statistics). This combination has become the basis for a new paradigm in model based inference. The text advocates formal inference from all the hypotheses/models in the a priori setâmultimodel inference. This compelling approach allows a simple ranking of the science hypothesis and their models. Simple methods are introduced for computing the likelihood of model i, given the data; the probability of model i, given the data; and evidence ratios. These quantities represent a formal strength of evidence and are easy to compute and understand, given the estimated model parameters and associated quantities (e.g., residual sum of squares, maximized log-likelihood, and covariance matrices). Additional forms of multimodel inference include model averaging, unconditional variances, and ways to rank the relative importance of predictor variables. This textbook is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals in various universities, agencies or institutes. Readers are 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. DAVID R. ANDERSON retired recently from serving as a senior scientist with the U.S. Geological Survey and professor in the Department of Fish, Wildlife, and Conservation Biology at Colorado State University. He has an emeritus professorship at CSU and is president of the Applied Information Company in Fort Collins. He has authored 18 scientific books and research monographs and over 100 journal publications. He has received a variety of awards, including U.S. Department of Interiorâs Meritorious Service Award and The Wildlife Societyâs 2004 Aldo Leopold Memorial Award and Medal.:
Contents note Introduction--science hypotheses and science philosophy -- Data and models -- Information theory and entropy -- Quantifying the evidence about science hypotheses -- Multimodel inference -- Advanced topics -- Summary.
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
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