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ORCIDSIAM/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, Deep Ray, Tanmay Sarkar:
Multilevel Monte Carlo Finite Difference Methods for Fractional Conservation Laws with Random Data. 65-105 - Mahadevan Ganesh, Frances Y. Kuo, Ian H. Sloan:
Quasi-Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media. 106-134 - Matthew Dobson, Yao Li, Jiayu Zhai:
Using Coupling Methods to Estimate Sample Quality of Stochastic Differential Equations. 135-162 - Alen Alexanderian, Noemi Petra, Georg Stadler, Isaac Sunseri:
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, Botond Szabó:
Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel? 185-230 - Wenyu Li, Arun Hegde, James Oreluk, Andrew K. Packard, Michael Frenklach:
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, Art B. Owen, Florian Puchhammer:
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, Abhyuday Mandal, C. Devon Lin, Xinwei Deng:
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, Ian H. Sloan:
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, Simon Weissmann:
Fokker-Planck Particle Systems for Bayesian Inference: Computational Approaches. 446-482 - Wenbo Sun, Matthew Plumlee, Jingwen Hu, Jionghua (Judy) Jin:
Robust System Design with Limited Experimental Data and an Inexact Simulation Model. 483-506 - Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. 507-535 - Weifeng Zhao, Juntao Huang, Wen-An Yong:
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, Bruno Sudret:
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, Peter Jan van Leeuwen, Manuel Pulido:
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, Ajay Jasra, Kody J. H. Law, Matti Vihola:
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions. 763-787 - Thomas P. Prescott, Ruth E. Baker:
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling. 788-817 - Felipe Uribe, Iason Papaioannou, Youssef M. Marzouk, Daniel Straub:
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, Akhtar A. Khan, Miguel Sama, Hans-Jörg Starkloff, Christiane Tammer:
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, David J. Nott, Daniel Edward Pagendam:
A Semiautomatic Method for History Matching Using Sequential Monte Carlo. 1034-1063 - Amy Braverman, Jonathan Hobbs, Joaquim Teixeira, Michael Gunson:
Post hoc Uncertainty Quantification for Remote Sensing Observing Systems. 1064-1093 - Deanna Easley, Tyrus Berry:
A Higher Order Unscented Transform. 1094-1131 - Baptiste Broto, François Bachoc, Marine Depecker, Jean-Marc Martinez:
Gaussian Linear Approximation for the Estimation of the Shapley Effects. 1132-1151 - Valentin Resseguier, Agustin M. Picard, Étienne Mémin, Bertrand Chapron:
Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models. 1152-1183 - Alex Bespalov, Dirk Praetorius, Michele Ruggeri:
Two-Level a Posteriori Error Estimation for Adaptive Multilevel Stochastic Galerkin Finite Element Method. 1184-1216 - Wei Fang, Mike B. Giles:
Importance Sampling for Pathwise Sensitivity of Stochastic Chaotic Systems. 1217-1241 - Michal Branicki, Kenneth Uda:
Lagrangian Uncertainty Quantification and Information Inequalities for Stochastic Flows. 1242-1313 - Dhruv V. Patel, Assad A. Oberai:
GAN-Based Priors for Quantifying Uncertainty in Supervised Learning. 1314-1343
Volume 9, Number 4, 2021
- Xujia Zhu, Bruno Sudret:
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, Giovanni Rabitti, Emanuele Borgonovo:
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, Cheng Li, Yuefeng Wu, Pu Zhang:
A Nonparametric Bayesian Framework for Uncertainty Quantification in Stochastic Simulation. 1527-1552 - Bangti Jin, Zehui Zhou, Jun Zou:
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, Serge Guillas:
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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