Доступ предоставлен для: Guest
International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 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

INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS

Том 2, Выпуск 4, 2012, pp. 397-412
DOI: 10.1615/Int.J.UncertaintyQuantification.2012004074
Get accessDownload

Краткое описание

The probability density function (PDF), and its corresponding cumulative density function (CDF), provide direct statistical insight into the characterization of a random process or field. Typically displayed as a histogram, one can infer probabilities of the occurrence of particular events. When examining a field over some two-dimensional domain in which at each point a PDF of the function values is available, it is challenging to assess the global (stochastic) features present within the field. In this paper, we present a visualization system that allows the user to examine two-dimensional data sets in which PDF (or CDF) information is available at any position within the domain. The tool provides a contour display showing the normed difference between the PDFs and an ansatz PDF selected by the user and, furthermore, allows the user to interactively examine the PDF at any particular position. Canonical examples of the tool are provided to help guide the reader into the mapping of stochastic information to visual cues along with a description of the use of the tool for examining data generated from an uncertainty quantification exercise accomplished within the field of electrophysiology.

ЦИТИРОВАНО В
  1. Pöthkow Kai, Hege Hans-Christian, Nonparametric Models for Uncertainty Visualization, Computer Graphics Forum, 32, 3pt2, 2013. Crossref

  2. Dao Tien Tuan, Ho Ba Tho Marie-Christine, Modeling of Biomechanical Data Uncertainty, in Biomechanics of the Musculoskeletal System, 2014. Crossref

  3. Chaudhuri Abon, Tzu Hsuan Wei , Teng Yok Lee , Han Wei Shen , Peterka Tom, Efficient Range Distribution Query for Visualizing Scientific Data, 2014 IEEE Pacific Visualization Symposium, 2014. Crossref

  4. He Yanyan, Mirzargar Mahsa, Kirby Robert M., Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory, International Journal of Approximate Reasoning, 66, 2015. Crossref

  5. Liebmann T., Scheuermann G., Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids, Computer Graphics Forum, 35, 3, 2016. Crossref

  6. Rosen Paul, Burton Brett, Potter Kristin, Johnson Chris R., muView: A Visual Analysis System for Exploring Uncertainty in Myocardial Ischemia Simulations, in Visualization in Medicine and Life Sciences III, 2016. Crossref

  7. Jarema Mihaela, Demir Ismail, Kehrer Johannes, Westermann Rudiger, Comparative visual analysis of vector field ensembles, 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), 2015. Crossref

  8. Hummel M., Jöckel L., Schäfer J., Hlawitschka M. W., Garth C., Visualizing Probabilistic Multi‐Phase Fluid Simulation Data using a Sampling Approach, Computer Graphics Forum, 36, 3, 2017. Crossref

  9. Ma Chihua, Luciani Timothy, Terebus Anna, Liang Jie, Marai G. Elisabeta, PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space, BMC Bioinformatics, 18, S2, 2017. Crossref

  10. Liu Can, Li Yanda, Yang Changhe, Yuan Xiaoru, Event-based exploration and comparison on time-varying ensembles, Journal of Visualization, 23, 1, 2020. Crossref

  11. Glaßer Sylvia, Hirsch Jan, Berg Philipp, Saalfeld Patrick, Beuing Oliver, Janiga Gabor, Preim Bernhard, Evaluation of Time-Dependent Wall Shear Stress Visualizations for Cerebral Aneurysms, in Bildverarbeitung für die Medizin 2016, 2016. Crossref

  12. Potter Kristin, Rosen Paul, Johnson Chris R., From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches, in Uncertainty Quantification in Scientific Computing, 377, 2012. Crossref

  13. Yan Lin, Wang Yusu, Munch Elizabeth, Gasparovic Ellen, Wang Bei, A Structural Average of Labeled Merge Trees for Uncertainty Visualization, IEEE Transactions on Visualization and Computer Graphics, 26, 1, 2020. Crossref

  14. Hullman Jessica, Why Authors Don't Visualize Uncertainty, IEEE Transactions on Visualization and Computer Graphics, 26, 1, 2020. Crossref

  15. Luther Wolfram, Auer Ekaterina, Weyers Benjamin, Reliable Visual Analytics, a Prerequisite for Outcome Assessment of Engineering Systems, Acta Cybernetica, 24, 3, 2020. Crossref

  16. Kleemann Timm, Ziegler Jürgen, Distribution sliders, Proceedings of the Conference on Mensch und Computer, 2020. Crossref

  17. Kim Yea-Seul, Kayongo Paula, Grunde-McLaughlin Madeleine, Hullman Jessica, Bayesian-Assisted Inference from Visualized Data, IEEE Transactions on Visualization and Computer Graphics, 27, 2, 2021. Crossref

  18. Athawale Tushar M., Sane Sudhanshu, Johnson Chris R., Uncertainty Visualization of the Marching Squares and Marching Cubes Topology Cases, 2021 IEEE Visualization Conference (VIS), 2021. Crossref

  19. Athawale Tushar M., Maljovec Dan, Yan Lin, Johnson Chris R., Pascucci Valerio, Wang Bei, Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps, IEEE Transactions on Visualization and Computer Graphics, 28, 4, 2022. Crossref

  20. Athawale Tushar, Johnson Chris R., Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data, IEEE Transactions on Visualization and Computer Graphics, 25, 1, 2019. Crossref

  21. Lin Haihan, Akbaba Derya, Meyer Miriah, Lex Alexander, Data Hunches: Incorporating Personal Knowledge into Visualizations, IEEE Transactions on Visualization and Computer Graphics, 29, 1, 2023. Crossref

Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции Цены и условия подписки Begell House Контакты Language English 中文 Русский Português German French Spain