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

by David R. Anderson

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.

208 pages, Kindle Edition

First published December 17, 2007

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Reviews

[Caley Brennan · Rating 5 out of 5]

I was once told that no one should use AIC for model selection without reading this book first and I definitely can’t disagree, having finally done so myself. A great introduction to AIC and the statistical philosophies behind it and similar concepts.

[Jason Yang · Rating 5 out of 5]

My second recent 'for-fun' textbook. I thought this was extremely well written. Anderson makes a concise case for using mathematical models as tools for inferring structure in biological processes. He simplifies statistical theory in palatable concepts which seem very tractable to apply. I picked this up to supplement my understanding of information theoretic metrics for comparing different models and found that this text gave me new ideas to test in my project.

[Anny · Rating 5 out of 5]

Clear, succinct, and practical.

[Adam · Rating 4 out of 5]

Solid all-around introduction to the theory and scientific application of AIC in model selection.