Advances in info-metrics: information and information processing across disciplines
"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), B...
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| Weitere Verfasser: | , , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Oxford University Press
[2021]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "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"-- |
| Beschreibung: | xviii, 538 Seiten Illustrationen, Diagramme 26 cm |
| ISBN: | 9780190636685 |
Internformat
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| 520 | 3 | |a "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"-- | |
| 653 | 0 | |a Information theory / Statistical methods | |
| 653 | 0 | |a Uncertainty (Information theory) | |
| 653 | 0 | |a Information theory / Statistical methods | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents Preface The Info-Metrics Institute Acknowledgments Contributor List ix xiii xv xvii PART I. INFORMATION, MEANING, AND VALUE 1. Information and Its Value J. Michael Dunn and Amos Golan 3 2. A Computational Theory of Meaning Pieter Adriaans 32 PART II. INFORMATION THEORY AND BEHAVIOR 3. Inferring the Logic of Collective Information Processors Bryan C. Daniels 81 4. Information-Theoretic Perspective on Human Ability Hwan-sik Choi 5. Information Recovery Related to Adaptive Economic Behavior and Choice George Judge 113 145 PARTIIL INFO-METRICS AND THEORY CONSTRUCTION 6. Maximum Entropy: A Foundation for a Unified Theory of Ecology John Harte 7. Entropie Dynamics: Mechanics without Mechanism Ariel Caticha 161 185
vi CONTENTS PART IV. INFO-METRICS IN ACTION I: PREDICTION AND FORECASTS 8. Toward Deciphering of Cancer Imbalances: Using Information-Theoretic Surprisal Analysis for Understanding of Cancer Systems Nataly Kravchenko-Balasha 215 9. Forecasting Socioeconomic Distributions on Small-Area Spatial Domains for Count Data Rosa Bernardini Papalta and Esteban Fernandez-Vazquez 240 10. Performance and Risk Aversion of Funds with Benchmarks: A Large Deviations Approach F. Douglas Foster and Michael Stutzer 264 11. Estimating Macroeconomic Uncertainty and Discord: Using Info-Metrics Kajal Lahiri and Wuwei Wang 290 12. Reduced Perplexity: A Simplified Perspective on Assessing Probabilistic Forecasts Kenric P. Nelson 325 PART V. INFO-METRICS IN ACTION II: STATISTICAL AND ECONOMETRICS INFERENCE 13. Info-metric Methods for the Estimation of Models with Group-Specific Moment Conditions Martyn Andrews, Alastair R. Hall, Rabeya Khatoon, and James Lincoln 349 14. Generalized Empirical Likelihood-Based Kernel Estimation of Spatially Similar Densities Kuangyu Wen and Ximing Wu 385 15. Rényi Divergence and Monte Carlo Integration John Geweke and Garland Durham 400 PART VI. INFO-METRICS, DATA INTELLIGENCE, AND VISUAL COMPUTING 16. Cost-Benefit Analysis of Data Intelligence—Its Broader Interpretations Min Chen 433
CONTENTS 17. The Role of the Information Channel in Visual Computing Miquel Feixas and Mateu Sbert VÌI 464 PART VII. INFO-METRICS AND NONPARAMETRIC INFERENCE 18. Entropy-Based Model Averaging Estimation of Nonparametric Models Yundong Tu 493 19. Information-Theoretic Estimation of Econometric Functions Millie Yi Mao and Aman Ullah 507 Index 531
|
| adam_txt |
Contents Preface The Info-Metrics Institute Acknowledgments Contributor List ix xiii xv xvii PART I. INFORMATION, MEANING, AND VALUE 1. Information and Its Value J. Michael Dunn and Amos Golan 3 2. A Computational Theory of Meaning Pieter Adriaans 32 PART II. INFORMATION THEORY AND BEHAVIOR 3. Inferring the Logic of Collective Information Processors Bryan C. Daniels 81 4. Information-Theoretic Perspective on Human Ability Hwan-sik Choi 5. Information Recovery Related to Adaptive Economic Behavior and Choice George Judge 113 145 PARTIIL INFO-METRICS AND THEORY CONSTRUCTION 6. Maximum Entropy: A Foundation for a Unified Theory of Ecology John Harte 7. Entropie Dynamics: Mechanics without Mechanism Ariel Caticha 161 185
vi CONTENTS PART IV. INFO-METRICS IN ACTION I: PREDICTION AND FORECASTS 8. Toward Deciphering of Cancer Imbalances: Using Information-Theoretic Surprisal Analysis for Understanding of Cancer Systems Nataly Kravchenko-Balasha 215 9. Forecasting Socioeconomic Distributions on Small-Area Spatial Domains for Count Data Rosa Bernardini Papalta and Esteban Fernandez-Vazquez 240 10. Performance and Risk Aversion of Funds with Benchmarks: A Large Deviations Approach F. Douglas Foster and Michael Stutzer 264 11. Estimating Macroeconomic Uncertainty and Discord: Using Info-Metrics Kajal Lahiri and Wuwei Wang 290 12. Reduced Perplexity: A Simplified Perspective on Assessing Probabilistic Forecasts Kenric P. Nelson 325 PART V. INFO-METRICS IN ACTION II: STATISTICAL AND ECONOMETRICS INFERENCE 13. Info-metric Methods for the Estimation of Models with Group-Specific Moment Conditions Martyn Andrews, Alastair R. Hall, Rabeya Khatoon, and James Lincoln 349 14. Generalized Empirical Likelihood-Based Kernel Estimation of Spatially Similar Densities Kuangyu Wen and Ximing Wu 385 15. Rényi Divergence and Monte Carlo Integration John Geweke and Garland Durham 400 PART VI. INFO-METRICS, DATA INTELLIGENCE, AND VISUAL COMPUTING 16. Cost-Benefit Analysis of Data Intelligence—Its Broader Interpretations Min Chen 433
CONTENTS 17. The Role of the Information Channel in Visual Computing Miquel Feixas and Mateu Sbert VÌI 464 PART VII. INFO-METRICS AND NONPARAMETRIC INFERENCE 18. Entropy-Based Model Averaging Estimation of Nonparametric Models Yundong Tu 493 19. Information-Theoretic Estimation of Econometric Functions Millie Yi Mao and Aman Ullah 507 Index 531 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T16:39:32Z |
| indexdate | 2024-07-10T09:04:14Z |
| institution | BVB |
| isbn | 9780190636685 |
| language | English |
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| spelling | Advances in info-metrics information and information processing across disciplines editors: Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah New York, NY Oxford University Press [2021] © 2021 xviii, 538 Seiten Illustrationen, Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier "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"-- Information theory / Statistical methods Uncertainty (Information theory) (DE-588)4143413-4 Aufsatzsammlung gnd-content Chen, Min 1960- (DE-588)121504298 edt Dunn, J. Michael 1941-2021 (DE-588)124999123 edt Golan, Amos (DE-588)133666085 edt Ullah, Aman 1946- (DE-588)112170048 edt Erscheint auch als Online-Ausgabe 9780190636715 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032562846&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Advances in info-metrics information and information processing across disciplines |
| subject_GND | (DE-588)4143413-4 |
| title | Advances in info-metrics information and information processing across disciplines |
| title_auth | Advances in info-metrics information and information processing across disciplines |
| title_exact_search | Advances in info-metrics information and information processing across disciplines |
| title_exact_search_txtP | Advances in info-metrics information and information processing across disciplines |
| title_full | Advances in info-metrics information and information processing across disciplines editors: Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah |
| title_fullStr | Advances in info-metrics information and information processing across disciplines editors: Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah |
| title_full_unstemmed | Advances in info-metrics information and information processing across disciplines editors: Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah |
| title_short | Advances in info-metrics |
| title_sort | advances in info metrics information and information processing across disciplines |
| title_sub | information and information processing across disciplines |
| topic_facet | Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032562846&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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