Advances in info-metrics a cross-disciplinary perspective of information and information processing / editors, Min Chen, J. Michael Dunn, Amos Golan, Aman Ullah.

Format Electronic
Publication InfoNew york : Oxford University Press, 2020.
Descriptionpages cm
Supplemental ContentFull text available from Oxford Scholarship Online
Subjects
Information theory--Statistical methods.
Uncertainty (Information theory)

Other author/creatorChen, Min, 1960 May 25-
Other author/creatorDunn, J. Michael, 1941-
Other author/creatorGolan, Amos.
Other author/creatorUllah, Aman.
Other author/creatorOxford University Press.
Abstract "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"-- Provided by publisher.
Bibliography noteIncludes bibliographical references and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2020021172
ISBN9780190636685 (hardback)
ISBN(epub)