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Advances in Info-Metrics expands the study of info-metrics - a framework for modeling, reasoning, and drawing inferences under conditions of insufficient information - across disciplines. This volume explores the mathematical and philosophical foundations of information-theoretic inference and demonstrates how to solve problems using new cross-disciplinary case studies and examples.
Inhaltsverzeichnis
- Part I. Information, Meaning and Value
- 1. Information and its Value J. Michael Dunn and Amos Golan
- 2. A Computational Theory of Meaning Pieter Adriaans
- Part II. Information Theory and Behavior
- 3. Inferring the Logic of Collective Information Processors Bryan C. Daniels
- 4. Information Theoretic Perspective on Human Ability Hwan-sik Choi
- 5. Information Recovery Related to Adaptive Economic Behavior and Choice George Judge
- Part III. Info-metrics and Theory Construction
- 6. Maximum Entropy: A Foundation for a Unified Theory of Ecology John Harte
- 7. Entropic Dynamics: Mechanics without Mechanism Ariel Caticha
- Part IV. Info-metrics in Action I: Prediction and Forecasts
- 8. Towards Deciphering of Cancer Imbalances: Using Information Theoretic Surprisal Analysis for Understanding of Cancer Systems Nataly Kravchenko-Balasha
- 9. Forecasting Socio Economic Distributions on Small Area Spatial Domains for Count Data
- Rosa Bernardini Papalia and Esteban Fernandez-Vazquez
- 10. Performance and Risk Aversion of Funds with Benchmarks: A Large Deviations Approach F. Douglas Foster and Michael Stutzer
- 11. Estimating Macroeconomic Uncertainty and Discord Using Info-Metrics Kajal Lahiri and Wuwei Wang
- 12. Reduced perplexity: A simplified perspective on assessing probabilistic forecasts Kenric P. Nelson
- Part V. Info-metrics in Action II: Statistical and Econometrics Inference
- 13. Info-metric Methods for the Estimation of Models with Group-Specifc Moment Conditions Martyn Andrews, Alastair R. Hall, Rabeya Khatoony, and James Lincoln
- 14. Generalized Empirical Likelihood Based Kernel Estimation of Spatially Similar Densities Kuangyu Wen and Ximing Wu
- 15. Rényi Divergence and Monte Carlo Integration John Geweke and Garland Durham
- Part VI. Info-metrics, Data Intelligence and Visualization
- 16. Cost-Benefit Analysis of Data Intelligence - Its Broader Interpretations Min Chen
- 17. The Role of Information Channel in Visual Computing Miquel Feixas and Mateu Sbert
- Part VII. Info-metrics and Nonparametric Inference
- 18. Entropy-based Model Averaging Estimation of Nonparametric Models Yundong Tu
- 19. Information Theoretic Estimation of Econometric Functions Millie Yi Mao and Aman Ullah
Über den Autor / die Autorin
Min Chen is the Professor of Scientific Visualization at Oxford University and a fellow of Pembroke College. He has co-authored over 200 publications, including his recent contributions in areas such as theory of visualization, video visualization, visual analytics, and perception and cognition in visualization.
J. Michael Dunn is Oscar Ewing Professor Emeritus of Philosophy, Professor Emeritus of Informatics and Computer Science, at Indiana University, where he spent most of his career and was founding dean of the School of Informatics. He is an affiliate member of the Info-Metrics Institute at the American University. His research has focused on information based logics.
Amos Golan is Professor of Economics and Director of the Info-Metrics Institute at American University. He is also an External Professor at the Santa Fe Institute and a Senior Associate at Pembroke College, Oxford. A leader in info-metrics, he is the author of Foundations of Info-Metrics: Information, Inference, and Incomplete Information.
Aman Ullah is Distinguished Professor of Economics at the University of California, Riverside. The author of 10 books and more than 160 published articles, Professor Ullah has helped shape the field of econometrics and has pioneered the development and application of non-parametric and semi-parametric methods.
Zusammenfassung
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 Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic 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 info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples.
Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.
Zusatztext
Impressive contributions in this volume address many aspects of information theory concepts, measures, and applications. It is a multidisciplinary tour de force, covering foundations, inference, and applications to finance, computing, behavioral models, and much more.
