no code implementations • 7 Feb 2022 • Marius Hofert, Avinash Prasad, Mu Zhu
This map, termed DecoupleNet, is used for dependence model assessment and selection.
no code implementations • 2 Dec 2021 • Marius Hofert, Avinash Prasad, Mu Zhu
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced.
no code implementations • 15 Dec 2020 • Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes.
no code implementations • 25 Feb 2020 • Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS).
1 code implementation • 1 Nov 2018 • Marius Hofert, Avinash Prasad, Mu Zhu
Once trained on pseudo-random samples from a parametric model or on real data, these neural networks only require a multivariate standard uniform randomized QMC point set as input and are thus fast in estimating expectations of interest under dependence with variance reduction.