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SIAM/ASA Journal on Uncertainty Quantification, Volume 9
Volume 9, Number 1, 2021
- Shuai Lu
, Pingping Niu, Frank Werner
On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise. 1-28 - Johnathan M. Bardsley, Tiangang Cui
Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems. 29-64 - Ujjwal Koley
Multilevel Monte Carlo Finite Difference Methods for Fractional Conservation Laws with Random Data. 65-105 - Mahadevan Ganesh
Quasi-Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media. 106-134 - Matthew Dobson
Using Coupling Methods to Estimate Sample Quality of Stochastic Differential Equations. 135-162 - Alen Alexanderian, Noemi Petra
Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know. 163-184 - Amine Hadji
Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel? 185-230 - Wenyu Li
Representing Model Discrepancy in Bound-to-Bound Data Collaboration. 231-259 - Michael Sinsbeck, Emily Cooke, Wolfgang Nowak:
Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence. 260-279 - Amal Ben Abdellah, Pierre L'Ecuyer
Density Estimation by Randomized Quasi-Monte Carlo. 280-301 - Takeru Matsuda, Yuto Miyatake
Estimation of Ordinary Differential Equation Models with Discretization Error Quantification. 302-331
Volume 9, Number 2, 2021
- Qian Xiao
EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors. 333-353 - Philipp A. Guth, Vesa Kaarnioja, Frances Y. Kuo, Claudia Schillings
A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty. 354-383 - Viet Ha Hoang, Jia Hao Quek, Christoph Schwab:
Multilevel Markov Chain Monte Carlo for Bayesian Inversion of Parabolic Partial Differential Equations under Gaussian Prior. 384-419 - Henning Omre, Kjartan Rimstad:
Bayesian Spatial Inversion and Conjugate Selection Gaussian Prior Models. 420-445 - Sebastian Reich
Fokker-Planck Particle Systems for Bayesian Inference: Computational Approaches. 446-482 - Wenbo Sun, Matthew Plumlee
Robust System Design with Limited Experimental Data and an Inexact Simulation Model. 483-506 - Denis Belomestny
Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. 507-535 - Weifeng Zhao
Lattice Boltzmann Method for Stochastic Convection-Diffusion Equations. 536-563 - Antoine Blanchard, Themistoklis P. Sapsis
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification. 564-592 - Nora Lüthen, Stefano Marelli
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark. 593-649 - Jingwei Hu, Lorenzo Pareschi, Yubo Wang:
Uncertainty Quantification for the BGK Model of the Boltzmann Equation Using Multilevel Variance Reduced Monte Carlo Methods. 650-680 - María Magdalena Lucini
Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter. 681-707 - Art B. Owen, Christopher R. Hoyt:
Efficient Estimation of the ANOVA Mean Dimension, with an Application to Neural Net Classification. 708-730 - Vishwas Rao, Mihai Anitescu
Efficient Computation of Extreme Excursion Probabilities for Dynamical Systems through Rice's Formula. 731-762 - Neil K. Chada, Jordan Franks
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions. 763-787 - Thomas P. Prescott
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling. 788-817 - Felipe Uribe
Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation. 818-847 - Ana Djurdjevac:
Linear Parabolic Problems in Random Moving Domains. 848-879 - Jean-Claude Fort, Thierry Klein, Agnès Lagnoux
Global Sensitivity Analysis and Wasserstein Spaces. 880-921 - Baasansuren Jadamba
A Convex Optimization Framework for the Inverse Problem of Identifying a Random Parameter in a Stochastic Partial Differential Equation. 922-952
Volume 9, Number 3, 2021
- Daniel Schaden, Elisabeth Ullmann
Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators. 953-978 - Matthieu Martin, Fabio Nobile
PDE-Constrained Optimal Control Problems with Uncertain Parameters using SAGA. 979-1012 - Zhaopeng Hao, Zhongqiang Zhang
Numerical Approximation of Optimal Convergence for Fractional Elliptic Equations with Additive Fractional Gaussian Noise. 1013-1033 - Christopher C. Drovandi
A Semiautomatic Method for History Matching Using Sequential Monte Carlo. 1034-1063 - Amy Braverman, Jonathan Hobbs
Post hoc Uncertainty Quantification for Remote Sensing Observing Systems. 1064-1093 - Deanna Easley
A Higher Order Unscented Transform. 1094-1131 - Baptiste Broto
Gaussian Linear Approximation for the Estimation of the Shapley Effects. 1132-1151 - Valentin Resseguier
Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models. 1152-1183 - Alex Bespalov
Two-Level a Posteriori Error Estimation for Adaptive Multilevel Stochastic Galerkin Finite Element Method. 1184-1216 - Wei Fang
Importance Sampling for Pathwise Sensitivity of Stochastic Chaotic Systems. 1217-1241 - Michal Branicki
Lagrangian Uncertainty Quantification and Information Inequalities for Stochastic Flows. 1242-1313 - Dhruv V. Patel
GAN-Based Priors for Quantifying Uncertainty in Supervised Learning. 1314-1343
Volume 9, Number 4, 2021
- Xujia Zhu
Emulation of Stochastic Simulators Using Generalized Lambda Models. 1345-1380 - Peng Chen, Omar Ghattas:
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters. 1381-1410 - Elmar Plischke
Computing Shapley Effects for Sensitivity Analysis. 1411-1437 - Mahmood Ettehad, Simon Foucart:
Instances of Computational Optimal Recovery: Dealing with Observation Errors. 1438-1456 - Panagiota Birmpa, Markos A. Katsoulakis
Uncertainty Quantification for Markov Random Fields. 1457-1498 - Matthew M. Dunlop, Yunan Yang
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion. 1499-1526 - Wei Xie
A Nonparametric Bayesian Framework for Uncertainty Quantification in Stochastic Simulation. 1527-1552 - Bangti Jin, Zehui Zhou
On the Saturation Phenomenon of Stochastic Gradient Descent for Linear Inverse Problems. 1553-1588 - Holger Dette, Anatoly A. Zhigljavsky
Reproducing Kernel Hilbert Spaces, Polynomials, and the Classical Moment Problem. 1589-1614 - Deyu Ming
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design. 1615-1642 - Jonathan Cockayne, Andrew B. Duncan:
Probabilistic Gradients for Fast Calibration of Differential Equation Models. 1643-1672 - Gildas Mazo
A Trade-Off Between Explorations and Repetitions for Estimators of Two Global Sensitivity Indices in Stochastic Models Induced by Probability Measures. 1673-1713
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