Scaling limits in computational Bayesian inversion
ESAIM: M2AN, 50 6 (2016) 1825-1856
Published online: 2016-10-21
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Parametric Shape Holomorphy of Boundary Integral Operators with Applications
Jürgen Dölz and Fernando HenríquezSIAM Journal on Mathematical Analysis 56 (5) 6731 (2024)
https://doi.org/10.1137/23M1576451
The Inverse of Exact Renormalization Group Flows as Statistical Inference
David S. Berman and Marc S. KlingerEntropy 26 (5) 389 (2024)
https://doi.org/10.3390/e26050389
Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference
Blair Bilodeau, Alex Stringer and Yanbo TangJournal of the American Statistical Association 119 (545) 690 (2024)
https://doi.org/10.1080/01621459.2022.2141635
460 431 (2024)
https://doi.org/10.1007/978-3-031-59762-6_21
Shape Holomorphy of Boundary Integral Operators on Multiple Open Arcs
José Pinto, Fernando Henríquez and Carlos Jerez-HanckesJournal of Fourier Analysis and Applications 30 (2) (2024)
https://doi.org/10.1007/s00041-024-10071-5
Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems
Tapio Helin and Remo KretschmannNumerische Mathematik 150 (2) 521 (2022)
https://doi.org/10.1007/s00211-021-01266-9
Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case
Jakob Zech and Youssef MarzoukConstructive Approximation 55 (3) 919 (2022)
https://doi.org/10.1007/s00365-022-09569-2
Isogeometric multilevel quadrature for forward and inverse random acoustic scattering
Jürgen Dölz, Helmut Harbrecht, Carlos Jerez-Hanckes and Michael MultererComputer Methods in Applied Mechanics and Engineering 388 114242 (2022)
https://doi.org/10.1016/j.cma.2021.114242
Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion
Martin Eigel, Robert Gruhlke and Manuel MarschallStatistics and Computing 32 (2) (2022)
https://doi.org/10.1007/s11222-022-10087-1
Stein Variational Reduced Basis Bayesian Inversion
Peng Chen and Omar GhattasSIAM Journal on Scientific Computing 43 (2) A1163 (2021)
https://doi.org/10.1137/20M1321589
Wavelet estimation of the dimensionality of curve time series
Rodney V. Fonseca and Aluísio PinheiroAnnals of the Institute of Statistical Mathematics 72 (5) 1175 (2020)
https://doi.org/10.1007/s10463-019-00724-4
On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems
Claudia Schillings, Björn Sprungk and Philipp WackerNumerische Mathematik 145 (4) 915 (2020)
https://doi.org/10.1007/s00211-020-01131-1
Multilevel Monte Carlo Estimation of the Expected Value of Sample Information
Tomohiko Hironaka, Michael B. Giles, Takashi Goda and Howard ThomSIAM/ASA Journal on Uncertainty Quantification 8 (3) 1236 (2020)
https://doi.org/10.1137/19M1284981
Advanced Multilevel Monte Carlo Methods
Ajay Jasra, Kody Law and Carina SuciuInternational Statistical Review 88 (3) 548 (2020)
https://doi.org/10.1111/insr.12365
Higher order Quasi-Monte Carlo integration for Bayesian PDE Inversion
Josef Dick, Robert N. Gantner, Quoc T. Le Gia and Christoph SchwabComputers & Mathematics with Applications 77 (1) 144 (2019)
https://doi.org/10.1016/j.camwa.2018.09.019
Generalized Modes in Bayesian Inverse Problems
Christian Clason, Tapio Helin, Remo Kretschmann and Petteri PiiroinenSIAM/ASA Journal on Uncertainty Quantification 7 (2) 652 (2019)
https://doi.org/10.1137/18M1191804
Uncertainty Quantification for Low-Frequency, Time-Harmonic Maxwell Equations with Stochastic Conductivity Models
Dimitris Kamilis and Nick PolydoridesSIAM/ASA Journal on Uncertainty Quantification 6 (4) 1295 (2018)
https://doi.org/10.1137/17M1156010
Sampling-free Bayesian inversion with adaptive hierarchical tensor representations
Martin Eigel, Manuel Marschall and Reinhold SchneiderInverse Problems 34 (3) 035010 (2018)
https://doi.org/10.1088/1361-6420/aaa998
Higher-Order Quasi-Monte Carlo for Bayesian Shape Inversion
R. N. Gantner and M. D. PetersSIAM/ASA Journal on Uncertainty Quantification 6 (2) 707 (2018)
https://doi.org/10.1137/16M1096116
Multilevel higher-order quasi-Monte Carlo Bayesian estimation
Josef Dick, Robert N. Gantner, Quoc T. Le Gia and Christoph SchwabMathematical Models and Methods in Applied Sciences 27 (05) 953 (2017)
https://doi.org/10.1142/S021820251750021X
Handbook of Uncertainty Quantification
Peng Chen and Christoph SchwabHandbook of Uncertainty Quantification 937 (2017)
https://doi.org/10.1007/978-3-319-12385-1_70
Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems
Peng Chen, Umberto Villa and Omar GhattasComputer Methods in Applied Mechanics and Engineering 327 147 (2017)
https://doi.org/10.1016/j.cma.2017.08.016
Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems
R. Scheichl, A. M. Stuart and A. L. TeckentrupSIAM/ASA Journal on Uncertainty Quantification 5 (1) 493 (2017)
https://doi.org/10.1137/16M1061692
Handbook of Uncertainty Quantification
Peng Chen and Christoph SchwabHandbook of Uncertainty Quantification 1 (2015)
https://doi.org/10.1007/978-3-319-11259-6_70-1