Authors: Kenneth P. Burnham, David Anderson
The second edition of this book is unique in that it focuses on methods for making formal
statistical inference from all the models in an a priori set (Multi-Model
Inference). A philosophy is presented for model-based data analysis and a general strategy outlined for the analysis of empirical data. The book invites increased attention on a priori science hypotheses and modeling. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected as an estimator of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various…
Burnham and
Anderson have put together a scholarly account of the developments in model selection techniques from the information theoretic viewpoint. This is an important practical subject. As… AIC is one of the widely known methods in model selection and inference.This book includes not only a basic use but also advanced issues of the information-theoretic approach. Using this book, you…
