Results 71 to 80 of about 89,598 (168)

Stepwise Variational Inference with Vine Copulas

CoRR
We propose stepwise variational inference (VI) with vine copulas: a universal VI procedure that combines vine copulas with a novel stepwise estimation procedure of the variational parameters. Vine copulas consist of a nested sequence of trees built from copulas, where more complex latent dependence can be modeled with increasing number of trees.
Elisabeth Griesbauer, Leiv Rønneberg, Arnoldo Frigessi, Claudia Czado, Ingrid Hobæk Haff   +4 more
openaire   +3 more sources

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, Volume 104, Issue 8, Page 4079-4100, August 2026.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Compound Impacts of Drought and Soil Salinity on Maize Productivity

Water Resources Research, Volume 62, Issue 8, August 2026.
Abstract Intensifying soil drought poses a significant threat to maize yields in China. However, the compounding effects of soil drought and salinity remain poorly understood. Here, we analyze maize yield trends in China from 1982 to 2016 and show that an increase in the total number of drought days is associated with significant yield declines.
Huiying Li, Shuo Wang
wiley   +1 more source

SL-vine and $Δ$-vine copula models

Traditional multivariate copulas often fail to capture complex dependence structures and may impose restrictive assumptions that limit their applicability to real-world data. To address these issues, vine copulas provide a flexible framework by constructing multivariate models from bivariate copulas.
Monggon, Tanabodee, Sumetkijakan, Songkiat, Santiwipanont, Tippawan, Kooakachai, Monchai   +3 more
openaire   +2 more sources

Truncation of vine copulas using fit indices

Journal of Multivariate Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eike C. Brechmann, Harry Joe
openaire   +1 more source

Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas [PDF]

Risk Analysis, 2015
Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of ...
Bedford, Tim, Daneshkhah, Alireza, Wilson, Kevin J.   +2 more
openaire   +4 more sources

Bayesian Nonparametric Inference for a Multivariate Copula Function [PDF]

, 2014
The paper presents a general Bayesian nonparametric approach for estimating a high dimensional copula. We first introduce the skew-normal copula, which we then extend to an infinite mixture model.
Wu, Juan, Wang, Xue, Walker, Stephen G.
core   +1 more source

Design flood estimation of cascade reservoirs based on vine-copula flood regional composition

Journal of Hydrology: Regional Studies
Study region: The study region is the Yalong River basin, the largest tributary of the Jinsha River in China. Study focus: The most likely flood regional composition (MLFRC) method has been widely used to estimate the design flood of cascade reservoirs ...
Sirui Zhong, Yanfeng He, Shenglian Guo, Yuzuo Xie, Chong-Yu Xu   +4 more
doaj   +1 more source

Bivariate postprocessing of wind vectors

Quarterly Journal of the Royal Meteorological Society, Volume 152, Issue 778, July 2026 Part A.
We introduce three novel bivariate postprocessing approaches and analyze their performance for joint postprocessing of bivariate wind‐vector components in Germany. Bivariate vine‐copula‐based models, a bivariate gradient‐boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared with
Ferdinand Buchner, David Jobst, Annette Möller, Claudia Czado   +3 more
wiley   +1 more source

ANALISIS STRUKTUR DEPENDENSI MULTIVARIAT MENGGUNAKAN VINE COPULA DENGAN MODEL C-VINE, D-VINE, DAN R-VINE COPULA

Copula merupakan suatu metode Statistika yang dapat digunakan untuk memodelkan struktur dependensi antar peubah acak dengan cara yang lebih sistematis dan terstruktur.
KARUNA, ELISABETH EVELIN, Jose, Rizal, Sigit, Nugroho   +2 more
core   +2 more sources