Transfer Entropy
Statistical relationships among the variables of a complex system reveal a lot about its physical behavior. Therefore, identification of the relevant variables and characterization of their interactions are crucial for a better understanding of a complex system. Linear methods, such as correlation,...
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| 格式: | Online |
| 語言: | 英语 |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| 主題: | |
| 在線閱讀: | 27499 |
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| _version_ | 1869526153452060672 |
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| author | Deniz Gençağa (Ed.) |
| author_browse | Deniz Gençağa (Ed.) |
| author_facet | Deniz Gençağa (Ed.) |
| author_sort | Deniz Gençağa (Ed.) |
| collection | Directory of Open Access Books |
| description | Statistical relationships among the variables of a complex system reveal a lot about its physical behavior. Therefore, identification of the relevant variables and characterization of their interactions are crucial for a better understanding of a complex system. Linear methods, such as correlation, are widely used to identify these relationships. However, information-theoretic quantities, such as mutual information and transfer entropy, have been proven to be superior in the case of nonlinear dependencies. Mutual information quantifies the amount of information obtained about one random variable through the other random variable, and it is symmetric. As an asymmetrical measure, transfer entropy quantifies the amount of directed (time-asymmetric) transfer of information between random processes and, thus, it is related to concepts, such as the Granger causality. This Special Issue includes 16 papers elucidating the state of the art of data-based transfer entropy estimation techniques and applications, in areas such as finance, biomedicine, fluid dynamics and cellular automata. Analytical derivations in special cases, improvements on the estimation methods and comparisons between certain techniques are some of the other contributions of this Special Issue. The diversity of approaches and applications makes this book unique as a single source of invaluable contributions from experts in the field. |
| format | Online |
| id | doab-20.500.12854ir-61186 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-611862024-03-30T12:51:13Z Transfer Entropy Deniz Gençağa (Ed.) T58.5-58.64 statistical signal processing entropy estimation nonlinear interactions data mining machine learning information-theoretic quantities causality information flow entropy correlation statistical dependency information-theory transfer entropy causal relationships mutual information Granger causality interacting subsystems thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Statistical relationships among the variables of a complex system reveal a lot about its physical behavior. Therefore, identification of the relevant variables and characterization of their interactions are crucial for a better understanding of a complex system. Linear methods, such as correlation, are widely used to identify these relationships. However, information-theoretic quantities, such as mutual information and transfer entropy, have been proven to be superior in the case of nonlinear dependencies. Mutual information quantifies the amount of information obtained about one random variable through the other random variable, and it is symmetric. As an asymmetrical measure, transfer entropy quantifies the amount of directed (time-asymmetric) transfer of information between random processes and, thus, it is related to concepts, such as the Granger causality. This Special Issue includes 16 papers elucidating the state of the art of data-based transfer entropy estimation techniques and applications, in areas such as finance, biomedicine, fluid dynamics and cellular automata. Analytical derivations in special cases, improvements on the estimation methods and comparisons between certain techniques are some of the other contributions of this Special Issue. The diversity of approaches and applications makes this book unique as a single source of invaluable contributions from experts in the field. 2021-02-12T06:22:00Z 2021-02-12T06:22:00Z 2018-08-24 17:15:19 2018 book 27499 9783038429203 9783038429197 https://directory.doabooks.org/handle/20.500.12854/61186 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://play.google.com/books/ http://www.mdpi.com/books https://doi.org/10.3390/books978-3-03842-920-3 MDPI - Multidisciplinary Digital Publishing Institute 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038429203 9783038429197 VIII, 326 open access |
| spellingShingle | T58.5-58.64 statistical signal processing entropy estimation nonlinear interactions data mining machine learning information-theoretic quantities causality information flow entropy correlation statistical dependency information-theory transfer entropy causal relationships mutual information Granger causality interacting subsystems thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Deniz Gençağa (Ed.) Transfer Entropy |
| title | Transfer Entropy |
| title_full | Transfer Entropy |
| title_fullStr | Transfer Entropy |
| title_full_unstemmed | Transfer Entropy |
| title_short | Transfer Entropy |
| title_sort | transfer entropy |
| topic | T58.5-58.64 statistical signal processing entropy estimation nonlinear interactions data mining machine learning information-theoretic quantities causality information flow entropy correlation statistical dependency information-theory transfer entropy causal relationships mutual information Granger causality interacting subsystems thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | T58.5-58.64 statistical signal processing entropy estimation nonlinear interactions data mining machine learning information-theoretic quantities causality information flow entropy correlation statistical dependency information-theory transfer entropy causal relationships mutual information Granger causality interacting subsystems thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | 27499 |
| work_keys_str_mv | AT denizgencagaed transferentropy |