Model Based Inference in the Life Sciences A Primer on Evidence [Paperback]

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.

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 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 seta 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 setmultimodel 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, givlãD