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Complexity Reduction in Analyzing Independence between Statistical Randomness Tests Using Mutual Information
Entropy. 2023. Vol. 25. No. 11. Article 1545.
Язык:
английский
Добавлено: 18 сентября 2026 г.
Karell-Albo J. A., Legón-Pérez C. M., Madarro-Capó E. J. и др., Entropy 2020 Vol. 22 No. 7 Article 741
Добавлено: 18 сентября 2026 г.
Чеповский А. А., Чеповский А. М., Успехи кибернетики 2025 Т. 6 № 1 С. 55–61
В настоящей работе авторы изучают проблему как оценки качества выделения неявных сообществ на графе, полученном при импорте данных из социальных сетей и мессенджерах, так и методов анализа таких сетей на предмет выявления информационного воздействия на ее акторов. Рассматривается два подхода к оценке корректности разбиения графа на сообщества. Первый способ оценки, основан на методах теории информации ...
Добавлено: 23 мая 2025 г.
Leydesdorff L., Иванова И. А., Journal of the Association for Information Science and Technology 2021 Vol. 72 No. 4 P. 387–402
Problem solving often requires crossing boundaries, such as those between disciplines. When policy‐makers call for “interdisciplinarity,” however, they often mean “synergy.” Synergy is generated when the whole offers more possibilities than the sum of its parts. An increase in the number of options above the sum of the options in subsets can be measured as ...
Добавлено: 30 октября 2020 г.
Иванов Ф. И., Крещук А. А., Рыбин П. С. и др., , in: The 11th International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT 2019).: Dublin: IEEE, 2019. P. 1–5.
Добавлено: 1 октября 2019 г.
Кельберт М. Я., Suhov Y., Stuhl I., Modern Stochastics: Theory and Applications 2017 Vol. 4 No. 3 P. 233–252
Добавлено: 10 октября 2017 г.
Осадчий А. Е., Journal of Neuroscience Methods 2012 Vol. 207 No. 1 P. 1–16
Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space–time,space–frequency, and space–time–frequency domains. ...
Добавлено: 23 октября 2014 г.
Pronko P., Baillet S., Pflieger M. и др., Frontiers in Neuroinformatics 2014 Vol. 7
Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of ...
Добавлено: 23 октября 2014 г.
Alexei Ossadtchi, Пронько П. К., Baillet S. и др., Frontiers in Neuroinformatics 2014 Vol. 7 No. January P. Article 53
Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of ...
Добавлено: 29 января 2014 г.
Ossadtchi A., Пронько П. К., Baillet S. и др., Frontiers in Neuroinformatics 2014 Vol. 7 No. 53 P. 1–11
Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of ...
Добавлено: 19 января 2014 г.