Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Uncertainty Quantification
Импакт фактор: 3.259 5-летний Импакт фактор: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

Свободный доступ

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2015011789
pages 123-137

BIVARIATE QUANTILE INTERPOLATION FOR ENSEMBLE DERIVED PROBABILITY DENSITY ESTIMATES

Brad Eric Hollister
Computer Science Department, Jack Baskin School of Engineering, 1156 High Street, University of California, Santa Cruz, California 95060, USA
Alex Pang
Computer Science Department, Jack Baskin School of Engineering, 1156 High Street, University of California, Santa Cruz, California 95060, USA

Краткое описание

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.


Articles with similar content:

RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 5
Josselin Garnier, Loic Le Gratiet
INCORPORATING PRIOR KNOWLEDGE FOR QUANTIFYING AND REDUCING MODEL-FORM UNCERTAINTY IN RANS SIMULATIONS
International Journal for Uncertainty Quantification, Vol.6, 2016, issue 2
Heng Xiao, Jin-Long Wu, Jianxun Wang
DATA ASSIMILATION FOR NAVIER-STOKES USING THE LEAST-SQUARES FINITE-ELEMENT METHOD
International Journal for Uncertainty Quantification, Vol.8, 2018, issue 5
Richard P. Dwight, Alexander Schwarz
AN ERROR SUBSPACE PERSPECTIVE ON DATA ASSIMILATION
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 6
Haiyan Cheng, Adrian Sandu
ADAPTIVE SELECTION OF SAMPLING POINTS FOR UNCERTAINTY QUANTIFICATION
International Journal for Uncertainty Quantification, Vol.7, 2017, issue 4
Casper Rutjes, Enrico Camporeale, Ashutosh Agnihotri