model selection and multimodel inference: a practical information-theoretic approach, second edition by kenneth p. burnham and david r. anderson. this book introduces graduate students and researchers in various scientific disciplines to information-theoretic methods for analyzing empirical data. these methods allow the selection of a "best" model and the ranking and weighting of other models in a predefined set. traditional statistical inference can then be based on the selected best model. however, the authors emphasize that information-theoretic approaches allow formal inference to be based on more than one model (multimodel inference). such procedures lead to more robust inferences in many cases, and the authors advocate these approaches throughout the book. the second edition was prepared with three goals in mind. first, the presentation of the material has been improved. boxes now highlight essential expressions and points. some reorganization has been done to improve the flow 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. second, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, particularly 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 the authors' experiences while giving several seminars, workshops, and graduate courses on the material in the first edition.model selection and multimodel inference: a practical information-theoretic approach, second edition by kenneth p. burnham and david r. anderson. this book introduces graduate students and researchers in various scientific disciplines to information-theoretic methods for analyzing empirical data. these methods allow the selection of a "best" model and the ranking and weighting of other models in a predefined set. traditional statistical inference can then be based on the selected best model. however, the authors emphasize that information-theoretic approaches allow formal inference to be based on more than one model (multimodel inference). such procedures lead to more robust inferences in many cases, and the authors advocate these approaches throughout the book. the second edition was prepared with three goals in mind. first, the presentation of the material has been improved. boxes now highlight essential expressions and points. some reorganization has been done to improve the flow 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. second, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, particularly 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 the authors' experiences while giving several seminars, workshops, and graduate courses on the material in the first edition.