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Statistical guarantees for Bayesian uncertainty quantification in non-linear inverse problems with Gaussian process priors
Annals of Statistics (Ann. Stat.), 2020
31 July 2020
F. Monard
Richard Nickl
G. Paternain
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Papers citing
"Statistical guarantees for Bayesian uncertainty quantification in non-linear inverse problems with Gaussian process priors"
21 / 21 papers shown
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Misspecified Bernstein-Von Mises theorem for hierarchical models
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On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations
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Consistent inference for diffusions from low frequency measurements
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A Bernstein--von-Mises theorem for the Calderón problem with piecewise constant conductivities
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Gaussian Process Regression in the Flat Limit
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Simon Barthelmé
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Nicolas M Tremblay
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Minimax detection of localized signals in statistical inverse problems
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Frank Werner
Axel Munk
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Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices
Computer Methods in Applied Mechanics and Engineering (CMAME), 2021
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Ieva Kazlauskaite
Eky Febrianto
F. Cirak
Mark Girolami
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Uncertainty quantification in the Bradley-Terry-Luce model
Information and Inference A Journal of the IMA (JIII), 2021
Chao Gao
Yandi Shen
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On some information-theoretic aspects of non-linear statistical inverse problems
Richard Nickl
G. Paternain
278
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20 Jul 2021
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems
Annales De L Institut Henri Poincare-probabilites Et Statistiques (IHPES), 2021
Jan Bohr
Richard Nickl
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17 May 2021
Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem
Inverse Problems (IP), 2021
Hanne Kekkonen
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Deep learning architectures for nonlinear operator functions and nonlinear inverse problems
Mathematical Statistics and Learning (MSL), 2019
Maarten V. de Hoop
Matti Lassas
C. Wong
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23 Dec 2019
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