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Bibliographies thématiques / Entropic uncertainty / Livres

Livres sur le sujet « Entropic uncertainty »

Pour voir les autres types de publications sur ce sujet consultez le lien suivant : Entropic uncertainty.

Auteur : Grafiati

Publié le 1 avril 2023

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Consultez les 18 meilleurs livres pour votre recherche sur le sujet « Entropic uncertainty ».

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1

Jessop, Alan. Informed assessments : An introduction to information, entropy, and statistics. New York : Ellis Horwood, 1995.

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2

Jessop, A. Informed assessments : An introduction to information, entropy and statistics. New York : Ellis Horwood, 1995.

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3

Watts, Barry D. Clausewitzian friction and future war. Washington, D.C : Institute for National Strategic Studies, National Defense University, 1996.

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4

Watts, Barry D. Clausewitzian friction and future war. Washington, D.C : Institute for National Strategic Studies, National Defense University, 1996.

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5

Watts, Barry D. Clausewitzian friction and future war. Washington, D.C : Institute for National Strategic Studies, National Defense University, 2004.

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6

Entropic Tokyo : Metropolis of Uncertainty, Multiplicity and Flexibility. LetteraVentidue Edizioni, 2021.

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7

Golan, Amos. The Metrics of Info-Metrics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0003.

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In this chapter I present the key ideas and develop the essential quantitative metrics needed for modeling and inference with limited information. I provide the necessary tools to study the traditional maximum-entropy principle, which is the cornerstone for info-metrics. The chapter starts by defining the primary notions of information and entropy as they are related to probabilities and uncertainty. The unique properties of the entropy are explained. The derivations and discussion are extended to multivariable entropies and informational quantities. For completeness, I also discuss the complete list of the Shannon-Khinchin axioms behind the entropy measure. An additional derivation of information and entropy, due to the independently developed work of Wiener, is provided as well.

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8

Nizami, Iftikhar R. *. Uncertainty in psychophysics : evidence for the entropy theory of perception. 1988.

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9

Hackworth, John. Essay Concerning the History of Entropy and the Rise of Uncertainty. BookBaby, 2016.

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10

Rajagopal. Market Entropy : How to Manage Chaos and Uncertainty for Improved Organizational Performance. Business Expert Press, 2020.

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11

Rajagopal et Rajagopal Rajagopal. Market Entropy : How to Manage Chaos and Uncertainty for Improved Organizational Performance. Business Expert Press, 2020.

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12

The Science of Disorder : Understanding the Complexity, Uncertainty, and Pollution in Our World. Los Feliz Pub., 2002.

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13

Watts, Barry D. Clausewitzian Friction And Future War. Diane Pub Co, 2004.

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14

Watts, Barry D. Clausewitzian Friction & Future War. Diane Pub Co, 1996.

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15

Golan, Amos. A Complete Info-Metrics Framework. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0009.

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In this chapter I develop the complete info-metrics framework for inferring problems and theories under all types of uncertainty and missing information. That framework allows for uncertainty in the observed values and about the functional form, as captured by the constraints. Using the derivations of Chapter 8, it also extends the info-metrics framework to include priors. The basic properties of the complete framework are developed as well. Generally speaking, that framework can be viewed as a “meta-theory”—a theory of how to construct theories and consistent models given the available information. This accrues all the benefits of the maximum entropy formalism but additionally accommodates a larger class of problems. The derivations are complemented with a complete visual representation of the info-metrics framework. Theoretical and empirical applications are provided.

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16

Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters : Part II, the maximum entropy approach. Tuscaloosa, Ala : Dept. of Mathematics, University of Alabama, 1989.

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17

National Aeronautics and Space Administration (NASA) Staff. Stochastic Model of the Nasa/Msfc Ground Facility for Large Space Structures with Uncertain Parameters : The Maximum Entropy Approach, Part 2. Independently Published, 2018.

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18

Chen, Min, J. Michael Dunn, Amos Golan et Aman Ullah, dir. Advances in Info-Metrics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190636685.001.0001.

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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 a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian methods published in the different proceedings started with the seminal collection of Levine and Tribus (1979) and continuing annually. The objective of this volume is to expand the study of info-metrics, and information processing, across the sciences and to further explore the basis of information-theoretic inference and its mathematical and philosophical foundations. This volume is inherently interdisciplinary and applications oriented. It contains some of the recent developments in the field, as well as many new cross-disciplinary case studies and examples. The emphasis here is on the interrelationship between information and inference where we view the word ‘inference’ in its most general meaning – capturing all types of problem solving. That includes model building, theory creation, estimation, prediction, and decision making. The volume contains nineteen chapters in seven parts. Although chapters in each part are related, each chapter is self-contained; it provides the necessary tools for using the info-metrics framework for solving the problem confronted in that chapter. This volume is designed to be accessible for researchers, graduate students, and practitioners across the disciplines, requiring only some basic quantitative skills. The multidisciplinary nature and applications provide a hands-on experience for the reader.

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