Model Selection and Multimodel Inference : A Practical Information Theoretic Approach Information and Likelihood Theory: A Basis for Model Selection and Inference Basic Use of the Information Theoretic Approach Formal Inference From More Than One Model: Multimodel Inference (MMI) Monte Carlo Insights and Extended Examples Advanced Issues and Deeper Insights Statistical Theory and Numerical Results Summary. We wrote this book to introduce graduate students and research workers in various scientic disciplines to the use of information theoretic approaches in the analysis of empirical data. These methods allow the data based selection of a “best” model and a ranking and weighting of the remaining models in a pre dened set. Traditional statistical inference can then be based on this selected best model. However we now emphasize that information theoretic approaches allow formal inference to be based on more than one model (m timodel inference). Such procedures lead to more robust inferences in many cases and we advocate these approaches throughout the book. The second edition was prepared with three goals in mind. First we have tried to improve the presentation of the material. Boxes now highlight ess tial expressions and points. Some reorganization has been done to improve the ow of concepts and a new chapter has been added. Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S ond concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book but p ticularly in Chapters 4 5 and 6. Third new technical material has been added to Chapters 5 and 6. Well over 100 new references to the technical literature are given. These changes result primarily from our experiences while giving several seminars workshops and graduate courses on material in the rst e tion.