likelihoods). If "no relationship" is really a plausible hypothesis, a null model can be included as an alternative rather than being assumed, as in hypothesis testing.
This slender 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.
It gave me a much better appreciation of the practical utility of the information theoretic approach. "We are not trying to model the data; instead, we are trying to model the information in the data. The goal is to recover the information that applies more generally to the process." It also corrects some common misinformation. For example, the factor 2K in Akaike's information criterion (where K is the number of parameters in a model) was not added to achieve parsimony of parameters, as I was taught. Rather, it is derived from information theory. That term accounts for the information lost from the data when it is used to estimate a parameter.
This would appear to be a good text for a one-quarter course in model-based inference. For those who are already familiar with model-based inference, perhaps from Hilborn and Mangel's The Ecological Detective, it provides a more indepth account of the information theoretical approach. For those who are new to model-based inference, it provides a good conceptual and technical introduction.