Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty.

In Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic inference and its mathematical and philosophical foundations. It emphasizes the interrelationship between information and inference and includes explanations of model building, theory creation, estimation, prediction, and decision making. Each of the nineteen chapters provides the necessary tools for using the info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples.

Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.