An Introduction to Transfer Entropy [electronic resource] : Information Flow in Complex Systems /
Bossomaier, Terry.author.
Barnett, Lionel.author.
Harré, Michael.author.
Lizier, Joseph T.author.
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.
Includes bibliographical references and index.
Introduction -- Statistical Preliminaries -- Information Theory -- Transfer Entropy -- Information Transfer in Canonical Systems -- Information Transfer in Financial Markets -- Miscellaneous Applications of Transfer Entropy -- Concluding Remarks.
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.
Artificial intelligence.
Applied mathematics.
Engineering mathematics.
Statistical physics.
Dynamical systems.
Neurosciences.
Computers.
Artificial Intelligence.
Mathematical and Computational Engineering.
Complex Systems.
Neurosciences.
Theory of Computation.
Statistical Physics and Dynamical Systems.