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ORCIDSIAM/ASA Journal on Uncertainty Quantification, Volume 8
Volume 8, Number 1, 2020
- Victoria Volodina, Daniel B. Williamson
:
Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures. 1-26 - Sharif Rahman:
A Spline Chaos Expansion. 27-57 - Owen R. Pembery, Euan A. Spence:
The Helmholtz Equation in Random Media: Well-Posedness and A Priori Bounds. 58-87 - Marie Kubínová, Ivana Pultarová:
Block Preconditioning of Stochastic Galerkin Problems: New Two-sided Guaranteed Spectral Bounds. 88-113 - Thomas P. Prescott, Ruth E. Baker:
Multifidelity Approximate Bayesian Computation. 114-138 - Laurent M. M. van den Bos, Benjamin Sanderse, Wim Bierbooms, Gerard J. W. van Bussel:
Generating Nested Quadrature Rules with Positive Weights based on Arbitrary Sample Sets. 139-169 - Emil M. Constantinescu, Noémi Petra, Julie Bessac, Cosmin G. Petra:
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms. 170-197 - Anthony Fillion, Marc Bocquet, Serge Gratton, Selime Gürol, Pavel Sakov:
An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error. 198-228 - Konstantinos Spiliopoulos:
Information Geometry for Approximate Bayesian Computation. 229-260 - Adi Ditkowski, Gadi Fibich, Amir Sagiv:
Density Estimation in Uncertainty Propagation Problems Using a Surrogate Model. 261-300 - Rubén Aylwin, Carlos Jerez-Hanckes, Christoph Schwab, Jakob Zech:
Domain Uncertainty Quantification in Computational Electromagnetics. 301-341 - Matteo Giordano, Hanne Kekkonen:
Bernstein-von Mises Theorems and Uncertainty Quantification for Linear Inverse Problems. 342-373 - Richard Nickl, Sara A. van de Geer, Sven Wang:
Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems. 374-413 - Assyr Abdulle, Andrea Di Blasio:
A Bayesian Numerical Homogenization Method for Elliptic Multiscale Inverse Problems. 414-450 - Jonas Latz:
On the Well-posedness of Bayesian Inverse Problems. 451-482 - Dan Crisan, Alberto López-Yela, Joaquín Míguez:
Stable Approximation Schemes for Optimal Filters. 483-509
Volume 8, Number 2, 2020
- Patrick Héas:
Selecting Reduced Models in the Cross-Entropy Method. 511-538 - Jeremiah Birrell, Luc Rey-Bellet:
Uncertainty Quantification for Markov Processes via Variational Principles and Functional Inequalities. 539-572 - Dave Osthus, Jeffrey D. Hyman, Satish Karra, Nishant Panda, Gowri Srinivasan:
A Probabilistic Clustering Approach for Identifying Primary Subnetworks of Discrete Fracture Networks with Quantified Uncertainty. 573-600 - Daniel Schaden, Elisabeth Ullmann:
On Multilevel Best Linear Unbiased Estimators. 601-635 - Kui Ren, Sarah Vallélian:
Characterizing Impacts of Model Uncertainties in Quantitative Photoacoustics. 636-667 - Matthias Heinkenschloss, Boris Kramer, Timur Takhtaganov:
Adaptive Reduced-Order Model Construction for Conditional Value-at-Risk Estimation. 668-692 - Baptiste Broto, François Bachoc, Marine Depecker:
Variance Reduction for Estimation of Shapley Effects and Adaptation to Unknown Input Distribution. 693-716 - Ying-Chao Hung, George Michailidis, Horace PakHai Lok:
Locating Infinite Discontinuities in Computer Experiments. 717-747 - Jürgen Dölz:
A Higher Order Perturbation Approach for Electromagnetic Scattering Problems on Random Domains. 748-774 - Olivier Roustant, Esperan Padonou, Yves Deville, Aloïs Clément, Guillaume Perrin, Jean Giorla, Henry P. Wynn:
Group Kernels for Gaussian Process Metamodels with Categorical Inputs. 775-806 - Gerardo Severino, Salvatore Cuomo:
Uncertainty Quantification of Unsteady Flows Generated by Line-Sources Through Heterogeneous Geological Formations. 807-825
Volume 8, Number 3, 2020
- Arpan Mukherjee, Rahul Rai, Puneet Singla, Tarunraj Singh, Abani K. Patra:
Overlapping Clustering Based Technique for Scalable Uncertainty Quantification in Physical Systems. 827-859 - Kevin Bulthuis, Frank Pattyn, Maarten Arnst:
A Multifidelity Quantile-Based Approach for Confidence Sets of Random Excursion Sets with Application to Ice-Sheet Dynamics. 860-890 - Matthias H. Y. Tan:
Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise. 891-925 - Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä:
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. 926-958 - Luc Pronzato, Anatoly Zhigljavsky:
Bayesian Quadrature, Energy Minimization, and Space-Filling Design. 959-1011 - Caroline Moosmüller, Felix Dietrich, Ioannis G. Kevrekidis:
A Geometric Approach to the Transport of Discontinuous Densities. 1012-1035 - Jeff Borggaard, Nathan E. Glatt-Holtz, Justin Krometis:
A Bayesian Approach to Estimating Background Flows from a Passive Scalar. 1036-1060 - Stéphane Crépey, Gersende Fort, Emmanuel Gobet, Uladzislau Stazhynski:
Uncertainty Quantification for Stochastic Approximation Limits Using Chaos Expansion. 1061-1089 - Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn:
Diffusion Map-based Algorithm for Gain Function Approximation in the Feedback Particle Filter. 1090-1117 - Lun Yang, Peng Wang, Daniel M. Tartakovsky:
Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation. 1118-1138 - Aaron R. Dinner, Erik H. Thiede, Brian Van Koten, Jonathan Weare:
Stratification as a General Variance Reduction Method for Markov Chain Monte Carlo. 1139-1188 - Martin Eigel, Manuel Marschall, Michael D. Multerer:
An Adaptive Stochastic Galerkin Tensor Train Discretization for Randomly Perturbed Domains. 1189-1214 - Linjie Wen, Jiangqi Wu, Linjun Lu, Jinglai Li:
A Defensive Marginal Particle Filtering Method for Data Assimilation. 1215-1235 - Tomohiko Hironaka, Michael B. Giles, Takashi Goda, Howard Thom:
Multilevel Monte Carlo Estimation of the Expected Value of Sample Information. 1236-1259
Volume 8, Number 4, 2020
- Marc Mignolet, Christian Soize:
Compressed Principal Component Analysis of Non-Gaussian Vectors. 1261-1286 - Kellin N. Rumsey, Gabriel Huerta, Justin Brown, Lauren Hund:
Dealing with Measurement Uncertainties as Nuisance Parameters in Bayesian Model Calibration. 1287-1309 - Aretha L. Teckentrup:
Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems. 1310-1337 - Yang Yu, Ning Zhang, Daniel W. Apley, Wenxin Jiang:
Including a Nugget Effect in Lifted Brownian Covariance Models. 1338-1357 - Pulong Ma:
Objective Bayesian Analysis of a Cokriging Model for Hierarchical Multifidelity Codes. 1358-1382 - Simon L. Cotter, Ioannis G. Kevrekidis, Paul T. Russell:
Transport Map Accelerated Adaptive Importance Sampling, and Application to Inverse Problems Arising from Multiscale Stochastic Reaction Networks. 1383-1413 - John Harlim, Daniel Sanz-Alonso, Ruiyi Yang:
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. 1414-1445 - Colin J. Cotter, Dan Crisan, Darryl D. Holm, Wei Pan, Igor Shevchenko:
A Particle Filter for Stochastic Advection by Lie Transport: A Case Study for the Damped and Forced Incompressible Two-Dimensional Euler Equation. 1446-1492 - Qin Li, Jian-Guo Liu, Ruiwen Shu:
Sensitivity Analysis of Burgers' Equation with Shocks. 1493-1521 - Rui Tuo, Yan Wang, C. F. Jeff Wu:
On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models. 1522-1547 - Nan Chen:
An Information Criterion for Choosing Observation Locations in Data Assimilation and Prediction. 1548-1573
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