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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

THE MUTUAL INFORMATION DIAGRAM FOR UNCERTAINTY VISUALIZATION

Volume 3, Issue 3, 2013, pp. 187-201
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003959
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ABSTRACT

We present a variant of the Taylor diagram, a type of two-dimensional plot that succinctly shows the relationship between two or more random variables based on their variance and correlation. The Taylor diagram has been adopted by the climate and geophysics communities to produce insightful visualizations, e.g., for intercomparison studies. Our variant, which we call the "mutual information diagram," represents the relationship between random variables in terms of their entropy and mutual information, and naturally maps well-known statistical quantities to their information-theoretic counterparts. Our new diagram is able to describe nonlinear relationships where linear correlation may fail; it allows for categorical and multivariate data to be compared; and it incorporates the notion of uncertainty, key in the study of large ensembles of data.

CITED BY
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  2. Gueymard Christian A., A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects, Renewable and Sustainable Energy Reviews, 39, 2014. Crossref

  3. Kim Jong Wook, Botvinnik Olga B, Abudayyeh Omar, Birger Chet, Rosenbluh Joseph, Shrestha Yashaswi, Abazeed Mohamed E, Hammerman Peter S, DiCara Daniel, Konieczkowski David J, Johannessen Cory M, Liberzon Arthur, Alizad-Rahvar Amir Reza, Alexe Gabriela, Aguirre Andrew, Ghandi Mahmoud, Greulich Heidi, Vazquez Francisca, Weir Barbara A, Van Allen Eliezer M, Tsherniak Aviad, Shao Diane D, Zack Travis I, Noble Michael, Getz Gad, Beroukhim Rameen, Garraway Levi A, Ardakani Masoud, Romualdi Chiara, Sales Gabriele, Barbie David A, Boehm Jesse S, Hahn William C, Mesirov Jill P, Tamayo Pablo, Characterizing genomic alterations in cancer by complementary functional associations, Nature Biotechnology, 34, 5, 2016. Crossref

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  6. Vahedian Fatemeh, Burke Robin, Mobasher Bamshad, Multirelational Recommendation in Heterogeneous Networks, ACM Transactions on the Web, 11, 3, 2017. Crossref

  7. Rovinelli Andrea, Guilhem Yoann, Proudhon Henry, Lebensohn Ricardo A, Ludwig Wolfgang, Sangid Michael D, Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework, Modelling and Simulation in Materials Science and Engineering, 25, 4, 2017. Crossref

  8. Rovinelli Andrea, Sangid Michael D., Proudhon Henry, Guilhem Yoann, Lebensohn Ricardo A., Ludwig Wolfgang, Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations, Journal of the Mechanics and Physics of Solids, 115, 2018. Crossref

  9. Madonna F., Rosoldi M., Güldner J., Haefele A., Kivi R., Cadeddu M. P., Sisterson D., Pappalardo G., Quantifying the value of redundant measurements at GCOS Reference Upper-Air Network sites, Atmospheric Measurement Techniques, 7, 11, 2014. Crossref

  10. Hanittinan Patinya, Tachikawa Yasuto, Ram‐Indra Teerawat, Projection of hydroclimate extreme indices over the Indochina region under climate change using a large single‐model ensemble, International Journal of Climatology, 40, 6, 2020. Crossref

  11. Zhang Minghu, Guo Jianwen, Li Xin, Jin Rui, Data-Driven Anomaly Detection Approach for Time-Series Streaming Data, Sensors, 20, 19, 2020. Crossref

  12. Nourani Vahid, Khodkar Kasra, Paknezhad Nardin Jabbarian, Laux Patrick, Deep learning-based uncertainty quantification of groundwater level predictions, Stochastic Environmental Research and Risk Assessment, 2022. Crossref

  13. Nourani Vahid, Khodkar Kasra, Gebremichael Mekonnen, Uncertainty assessment of LSTM based groundwater level predictions, Hydrological Sciences Journal, 67, 5, 2022. Crossref

  14. Despotovic Milan, Nedic Vladimir, Despotovic Danijela, Cvetanovic Slobodan, Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation, Renewable and Sustainable Energy Reviews, 56, 2016. Crossref

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