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A fully Bayesian sparse polynomial chaos expansion approach with joint
  priors on the coefficients and global selection of terms
v1v2 (latest)

A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms

12 April 2022
Paul-Christian Bürkner
Ilja Kroker
S. Oladyshkin
Wolfgang Nowak
ArXiv (abs)PDFHTML

Papers citing "A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms"

2 / 2 papers shown
Title
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Philipp Reiser
Javier Enrique Aguilar
A. Guthke
Paul-Christian Bürkner
151
3
0
08 Dec 2023
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep
  Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos
  Theory
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
S. Oladyshkin
T. Praditia
Ilja Kroker
F. Mohammadi
Wolfgang Nowak
S. Otte
AI4CE
48
5
0
26 Jun 2023
1