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Advances In Infometrics: Information And Information Processing Across Disciplines,New
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Infometrics 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 Decisionmaking Under Uncertainty.In Advances In Infometrics, Min Chen, J. Michael Dunn, Amos Golan, And Aman Ullah Bring Together A Group Of Thirty Experts To Expand The Study Of Infometrics Across The Sciences And Demonstrate How To Solve Problems Using This Interdisciplinary Framework. Building On The Theoretical Underpinnings Of Infometrics, The Volume Sheds New Light On Statistical Inference, Information, And General Problem Solving. The Book Explores The Basis Of Informationtheoretic 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 Infometrics Framework To Solve A Problem. The Collection Covers Recent Developments In The Field, As Well As Many New Crossdisciplinary Case Studies And Examples.Designed To Be Accessible For Researchers, Graduate Students, And Practitioners Across Disciplines, This Book Provides A Clear, Handson Experience For Readers Interested In Solving Problems When Presented With Incomplete And Imperfect Information.
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