Advances in info-metrics : a cross-disciplinary perspective of information and information processing - Chen, Min; Dunn, J. Michael; Golan, Amos; Ullah, Aman (Oxford University Press, 2020)
"Info-metrics is a framework for rational inference on the basis of limited, or insufficient, information. It is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. Info-metrics has its roots in information theory (Shannon, 1948), Bernoulli's and Laplace's principle of insufficient reason (Bernoulli, 1713) and its offspring the principle of maximum entropy (Jaynes, 1957). It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. Within a constrained optimization setup, info-metrics provides a simple way for modeling and understanding all types of systems and problems. It is a framework for processing the available information with minimal reliance on assumptions and information that cannot be validated. Quite often a model cannot be validated with finite data. Examples include biological, social and behavioral models, as well as models of cognition and knowledge. The info-metrics framework extends naturally for tackling these types of common problems"