Model Selection and Inference: A Practical Information-Theoretic Approach

by , ,

This work covers the philosophy of model-based data analysis and provides an omnibus strategy for the analysis of empirical data. It introduces information theoretical approaches and focuses critical attention on a priori modelling and the selection of a good approximating model that best represents the inference supported by the data. 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 to ...

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Model Selection and Inference: A Practical Information-Theoretic Approach
1998, Springer, New York, NY

ISBN-13: 9780387985046

Hardcover

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