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An introduction to transfer entropy : information flow in complex systems /
This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present informa...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham, Switzerland :
Springer,
©2016.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 3 | |a An introduction to transfer entropy : |b information flow in complex systems / |c Terry Bossomaier, Lionel Barnett, Michael Harré, Joseph T. Lizier. |
| 260 | |a Cham, Switzerland : |b Springer, |c ©2016. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 505 | 0 | |a Preface; Contents; List of Key Ideas; List of Open Research Questions; List of Key Results; Symbols; Acronyms; List of Tables; List of Figures; 1 Introduction; 1.1 Information Theory; 1.2 Complex Systems; 1.2.1 Cellular Automata; 1.2.2 Spin Models; 1.2.3 Oscillators; 1.2.4 Complex Networks; 1.2.5 Random Boolean Networks; 1.2.6 Flocking Behaviour; 1.3 Information Flow and Causality; 1.4 Applications; 1.5 Overview; 2 Statistical Preliminaries; 2.1 Set Theory; 2.2 Discrete Probabilities; 2.3 Conditional, Independent and Joint Probabilities; 2.3.1 Conditional Probabilities. | |
| 505 | 8 | |a 2.3.2 Independent Probabilities2.3.3 Joint Probabilities; 2.3.4 Conditional Independence; 2.3.5 Time-Series Data and Embedding Dimensions; 2.3.6 Conditional Independence and Markov Processes; 2.3.7 Vector Autoregression; 2.4 Statistical Expectations, Moments and Correlations; 2.5 Probability Distributions; 2.5.1 Binomial Distribution; 2.5.2 Poisson Distribution; 2.5.3 Continuous Probabilities; 2.5.4 Gaussian Distribution; 2.5.5 Multivariate Gaussian Distribution; 2.6 Symmetry and Symmetry Breaking; 3 Information Theory; 3.1 Introduction; 3.2 Basic Ideas; 3.2.1 Entropy and Information. | |
| 505 | 8 | |a 3.2.2 Mutual Information3.2.3 Conditional Mutual Information; 3.2.4 Kullback-Leibler Divergence; 3.2.5 Entropy of Continuous Processes; 3.2.6 Entropy and Kolmogorov Complexity; 3.2.7 Historical Note: Mutual Information and Communication; 3.3 Mutual Information and Phase Transitions; 3.4 Numerical Challenges; 3.4.1 Calculating Entropy; 3.4.2 Calculating Mutual Information; 3.4.3 The Non-stationary Case; 4 Transfer Entropy; 4.1 Introduction; 4.2 Definition of Transfer Entropy; 4.2.1 Determination of History Lengths; 4.2.2 Computational Interpretation as Information Transfer. | |
| 505 | 8 | |a 4.2.3 Conditional Transfer Entropy4.2.4 Source-Target Lag; 4.2.5 Local Transfer Entropy; 4.3 Transfer Entropy Estimators; 4.3.2 Symbolic Transfer Entropy; 4.3.1 KSG Estimation for Transfer Entropy; 4.3.3 Open-Source Transfer Entropy Software; 4.4 Relationship with Wiener-Granger Causality; 4.4.1 Granger Causality Captures Causality as Predictive of Effect; 4.4.2 Definition of Granger Causality; 4.4.3 Maximum-Likelihood Estimation of Granger Causality; 4.4.4 Granger Causality Versus Transfer Entropy; 4.5 Comparing Transfer Entropy Values; 4.5.1 Statistical Significance. | |
| 505 | 8 | |a 4.5.2 Normalising Transfer Entropy4.6 Information Transfer Density and Phase Transitions; 4.7 Continuous-Time Processes; 5 Information Transfer in Canonical Systems; 5.1 Cellular Automata; 5.2 Spin Models; 5.3 Random Boolean Networks; 5.4 Small-World Networks; 5.5 Swarming Models; 5.6 Synchronisation Processes; 5.7 Summary; 6 Information Transfer in Financial Markets; 6.1 Introduction to Financial Markets; 6.2 Information Theory Applied to Financial Markets; 6.2.1 Entropy and Economic Diversity: an Early Ecology of Economics; 6.2.2 Maximum Entropy: Maximum Diversity? | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors' work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance. The book will be of value to advanced undergraduate and graduate students and researchers in the areas of computer science, neuroscience, physics, and engineering. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Information theory. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Information Theory | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 6 | |a Théorie de l'information. | |
| 650 | 6 | |a Intelligence artificielle. | |
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| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Operations Research. |2 bisacsh | |
| 650 | 7 | |a Information theory |2 fast | |
| 700 | 1 | |a Bossomaier, Terry R. J. |q (Terry Richard John) |1 https://id.oclc.org/worldcat/entity/E39PCjryxHDcm7YFt44KGdTjFq | |
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