Suscripción a Biblioteca: Guest
International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 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

BIVARIATE QUANTILE INTERPOLATION FOR ENSEMBLE DERIVED PROBABILITY DENSITY ESTIMATES

Volumen 5, Edición 2, 2015, pp. 123-137
DOI: 10.1615/Int.J.UncertaintyQuantification.2015011789
Get accessDownload

SINOPSIS

Probability distribution functions (PDFs) may be estimated from members in an ensemble. For an ensemble of 2D vector fields, this results in a bivariate PDF at each location in the field. Vector field analysis and visualization, e.g., stream line calculation, require an interpolation to be defined over these 2D density estimates. Thus, a nonparametric PDF interpolation must advect features as opposed to cross-fading them, where arbitrary modalities in the distribution can be introduced. This is already achieved for 1D PDF interpolation via inverse cumulative distribution functions (CDFs). However, there is no closed-form extension to bivariate PDF. This paper presents one such direct extension of the 1D closed-form solution for bivariates. We show an example of physically coupled components (velocity) and correlated random variables. Our method does not require a complex implementation or expensive computation as does displacement interpolation Bonneel et al., ACM Trans. Graphics (TOG), 30(6):158, 2011. Additionally, our method does not suffer from ambiguous pair-wise linear interpolants, as does Gaussian Mixture Model Interpolation.

CITADO POR
  1. Ren Ke, Qu Dezhan, Xu Shaobin, Jiao Xufeng, Tai Liang, Zhang Huijie, Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field, ISPRS International Journal of Geo-Information, 9, 1, 2020. Crossref

  2. Athawale Tushar M., Ma Bo, Sakhaee Elham, Johnson Chris R., Entezari Alireza, Direct Volume Rendering with Nonparametric Models of Uncertainty, IEEE Transactions on Visualization and Computer Graphics, 27, 2, 2021. Crossref

  3. Kamal Aasim, Dhakal Parashar, Javaid Ahmad Y., Devabhaktuni Vijay K., Kaur Devinder, Zaientz Jack, Marinier Robert, Recent advances and challenges in uncertainty visualization: a survey, Journal of Visualization, 24, 5, 2021. Crossref

Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones Precios y Políticas de Suscripcione Begell House Contáctenos Language English 中文 Русский Português German French Spain