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On the Convergence of the Laplace Approximation and
  Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian
  Inverse Problems
v1v2v3v4 (latest)

On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems

13 January 2019
C. Schillings
Björn Sprungk
Philipp Wacker
ArXiv (abs)PDFHTML

Papers citing "On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems"

18 / 18 papers shown
Title
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems
Robert Gruhlke
Matei Hanu
Claudia Schillings
Philipp Wacker
BDL
101
0
0
17 Apr 2025
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Imad Aouali
Victor-Emmanuel Brunel
David Rohde
Anna Korba
OffRL
180
5
0
22 Feb 2024
Perspectives on locally weighted ensemble Kalman methods
Perspectives on locally weighted ensemble Kalman methods
Philipp Wacker
16
1
0
06 Jan 2024
How good is your Laplace approximation of the Bayesian posterior?
  Finite-sample computable error bounds for a variety of useful divergences
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikolaj Kasprzak
Ryan Giordano
Tamara Broderick
70
0
0
29 Sep 2022
Dimension free non-asymptotic bounds on the accuracy of high dimensional
  Laplace approximation
Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation
V. Spokoiny
78
22
0
23 Apr 2022
Robust random walk-like Metropolis-Hastings algorithms for concentrating
  posteriors
Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors
Daniel Rudolf
Björn Sprungk
60
6
0
24 Feb 2022
Bayesian neural network priors for edge-preserving inversion
Bayesian neural network priors for edge-preserving inversion
Chen Li
Matthew M. Dunlop
G. Stadler
46
13
0
20 Dec 2021
A Variational Inference Approach to Inverse Problems with Gamma
  Hyperpriors
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors
Shivendra Agrawal
Hwanwoo Kim
D. Sanz-Alonso
A. Strang
75
10
0
26 Nov 2021
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent
  Advances and Applications
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
Xinlei Zhou
Han Liu
Farhad Pourpanah
T. Zeng
Xizhao Wang
UQCVUD
124
61
0
03 Nov 2021
Multimodal Information Gain in Bayesian Design of Experiments
Multimodal Information Gain in Bayesian Design of Experiments
Q. Long
127
16
0
16 Aug 2021
On log-concave approximations of high-dimensional posterior measures and
  stability properties in non-linear inverse problems
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems
Jan Bohr
Richard Nickl
57
17
0
17 May 2021
A hybrid Gibbs sampler for edge-preserving tomographic reconstruction
  with uncertain view angles
A hybrid Gibbs sampler for edge-preserving tomographic reconstruction with uncertain view angles
Felipe Uribe
Johnathan M. Bardsley
Yiqiu Dong
P. Hansen
N. A. B. Riis
58
13
0
14 Apr 2021
The computational asymptotics of Gaussian variational inference and the
  Laplace approximation
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Zuheng Xu
Trevor Campbell
105
8
0
13 Apr 2021
Non-asymptotic error estimates for the Laplace approximation in Bayesian
  inverse problems
Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems
T. Helin
Remo Kretschmann
51
18
0
11 Dec 2020
Context-aware surrogate modeling for balancing approximation and
  sampling costs in multi-fidelity importance sampling and Bayesian inverse
  problems
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems
Terrence Alsup
Benjamin Peherstorfer
77
11
0
22 Oct 2020
Unbiased MLMC stochastic gradient-based optimization of Bayesian
  experimental designs
Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs
T. Goda
Tomohiko Hironaka
Wataru Kitade
Adam Foster
67
23
0
18 May 2020
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian
  Full Waveform Inversion
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion
Matthew M. Dunlop
Yunan Yang
136
12
0
07 Apr 2020
Advanced Multilevel Monte Carlo Methods
Advanced Multilevel Monte Carlo Methods
Ajay Jasra
K. Law
C. Suciu
73
16
0
24 Apr 2017
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