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题名/责任者:

Advances in info-metrics : information and information processing across disciplines / editors, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah.

出版发行项:

New York, NY : Oxford University Press, 2021.

ISBN:

9780190636685

载体形态项:

xviii, 538 pages : illustrations (some color) ; 24 cm

附加个人名称:

Chen, Min, 1960 May 25- editor.

附加个人名称:

Dunn, J. Michael, 1941- editor.

附加个人名称:

Golan, Amos, editor.

附加个人名称:

Ullah, Aman, editor.

论题主题:

Information theory-Statistical methods.

论题主题:

Uncertainty (Information theory)

中图法分类号:

G201

书目附注:

Includes bibliographical references and index.

摘要附注:

"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"--

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