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SIAM/ASA Journal on Uncertainty Quantification, Volume 11
Volume 11, Number 1, March 2023
- Mikkel Bue Lykkegaard
, Tim J. Dodwell, Colin Fox
Multilevel Delayed Acceptance MCMC. 1-30 - Babak Maboudi Afkham
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography. 31-61 - Wouter Edeling:
On the Deep Active-Subspace Method. 62-90 - Juan P. Madrigal-Cianci, Fabio Nobile, Raúl Tempone:
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis-Hastings. 91-138 - Martin Chak
On the Generalized Langevin Equation for Simulated Annealing. 139-167 - Cédric Travelletti, David Ginsbourger
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors. 168-198 - Christoph Schwab, Jakob Zech
Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\). 199-234 - Keyi Wu, Peng Chen, Omar Ghattas:
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design. 235-261 - Jan Glaubitz
Generalized Sparse Bayesian Learning and Application to Image Reconstruction. 262-284 - Terrence Alsup, Benjamin Peherstorfer:
Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems. 285-319 - Leon Bungert, Philipp Wacker
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion. 320-357 - Max Ehre
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method. 358-388
Volume 11, Number 2, June 2023
- Adrian N. Bishop, Pierre Del Moral:
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems. 389-425 - Christophette Blanchet-Scalliet, Bruno Demory, Thierry Gonon
Gaussian Process Regression on Nested Spaces. 426-451 - Fedor Goncharov
Nonparametric Posterior Learning for Emission Tomography. 452-479 - Maarten V. de Hoop, Nikola B. Kovachki
Convergence Rates for Learning Linear Operators from Noisy Data. 480-513 - Baptiste Kerleguer
Multifidelity Surrogate Modeling for Time-Series Outputs. 514-539 - Elaine T. Spiller
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models. 540-566 - Euan A. Spence
Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification. 567-590 - Tim Jahn:
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise. 591-615 - Jeremy Heng, Ajay Jasra
On Unbiased Estimation for Discretized Models. 616-645 - Ben Mansour Dia
A Continuation Method in Bayesian Inference. 646-681 - Yian Chen
Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices. 682-725
Volume 11, Number 3, September 2023
- Prerna Patil, Hessam Babaee:
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions. 727-756 - Claudia Schillings
Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling. 757-787 - Shanyin Tong
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions. 788-813 - Elliot Cartee, Antonio Farah, April Nellis
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes. 814-847 - Cécile Haberstich, Anthony Nouy, Guillaume Perrin:
Active Learning of Tree Tensor Networks using Optimal Least Squares. 848-876 - Christophe Audouze, Aaron Klein
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov-Galerkin Method. 877-905 - Sam Allen
Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores. 906-940 - Alexey Chernov, Erik Marc Schetzke:
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity. 941-969 - Teo Deveney
Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems. 970-995 - Nathaniel Pritchard
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification. 996-1024 - Jasper M. Everink
Bayesian Inference with Projected Densities. 1025-1043 - Vladimir G. Spokoiny
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation. 1044-1068 - Shurui Lv, Jun Yu, Yan Wang
Fast Calibration for Computer Models with Massive Physical Observations. 1069-1104
Volume 11, Number 4, December 2023
- Remo Kretschmann
Are Minimizers of the Onsager-Machlup Functional Strong Posterior Modes? 1105-1138 - Johannes Milz
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs. 1139-1163 - Yao Li
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems. 1164-1194 - Hefin Lambley
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems. 1195-1224 - Toni Karvonen
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation. 1225-1257 - Vilho Halonen
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks. 1258-1277 - Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. 1278-1307 - Sébastien Petit
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria. 1308-1328 - Seif Ben Bader
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology. 1329-1356 - Suraj Yerramilli, Akshay Iyer, Wei Chen, Daniel W. Apley:
Fully Bayesian Inference for Latent Variable Gaussian Process Models. 1357-1381
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