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Variational Bayesian Approximation of Inverse Problems using Sparse
  Precision Matrices

Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices

22 October 2021
Jan Povala
Ieva Kazlauskaite
Eky Febrianto
F. Cirak
Mark Girolami
ArXivPDFHTML

Papers citing "Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices"

9 / 9 papers shown
Title
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
Alex Glyn-Davies
A. Vadeboncoeur
O. Deniz Akyildiz
Ieva Kazlauskaite
Mark Girolami
PINN
58
0
0
10 Sep 2024
Weak neural variational inference for solving Bayesian inverse problems
  without forward models: applications in elastography
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
Vincent C. Scholz
Yaohua Zang
P. Koutsourelakis
34
2
0
30 Jul 2024
Variational Bayesian surrogate modelling with application to robust
  design optimisation
Variational Bayesian surrogate modelling with application to robust design optimisation
Thomas A. Archbold
Ieva Kazlauskaite
F. Cirak
18
1
0
23 Apr 2024
Variational Gaussian Processes For Linear Inverse Problems
Variational Gaussian Processes For Linear Inverse Problems
Thibault Randrianarisoa
Botond Szabó
26
3
0
01 Nov 2023
Stochastic PDE representation of random fields for large-scale Gaussian
  process regression and statistical finite element analysis
Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis
Kim Jie Koh
F. Cirak
AI4CE
22
9
0
23 May 2023
VI-DGP: A variational inference method with deep generative prior for
  solving high-dimensional inverse problems
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
Yingzhi Xia
Qifeng Liao
Jinglai Li
13
2
0
22 Feb 2023
Fully probabilistic deep models for forward and inverse problems in
  parametric PDEs
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
A. Vadeboncoeur
Ömer Deniz Akyildiz
Ieva Kazlauskaite
Mark Girolami
F. Cirak
AI4CE
15
17
0
09 Aug 2022
A Variational Inference Framework for Inverse Problems
A Variational Inference Framework for Inverse Problems
Luca Maestrini
R. Aykroyd
M. Wand
11
6
0
10 Mar 2021
Consistency of Bayesian inference with Gaussian process priors in an
  elliptic inverse problem
Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem
M. Giordano
Richard Nickl
26
57
0
16 Oct 2019
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