Cover Image

Consistency of Variational Inference for Nonlinear Inverse Problems of Partial Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zu, Shaokang, Jia, Junxiong, Meng, Deyu
Format:	Preprint
Published:	2024
Subjects:	Statistics Theory 65N21, 62G20
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Consistency of variational inference for Besov priors in non-linear inverse problems
by: Zu, Shaokang, et al.
Published: (2025)

Nonparametric Prior Learning in Differential Equation Modeling
by: Jia, Junxiong, et al.
Published: (2023)

Sequential Monte Carlo with Gaussian Mixture Approximation for Infinite-Dimensional Statistical Inverse Problems
by: Lu, Haoyu, et al.
Published: (2025)

On Learning the Optimal Regularization Parameter in Inverse Problems
by: Rodriguez, Jonathan Chirinos, et al.
Published: (2023)

Adaptive Gaussian Process Regression for Efficient Building of Surrogate Models in Inverse Problems
by: Semler, Phillip, et al.
Published: (2023)

Adaptive thresholding for wavelet-based nonparametric heteroskedastic variance estimation on the sphere
by: Durastanti, Claudio, et al.
Published: (2026)

Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
by: Ding, Zhao, et al.
Published: (2024)

An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths
by: Somé, Sobom M., et al.
Published: (2025)

Convergence Analysis of regularised Nyström method for Functional Linear Regression
by: Gupta, Naveen, et al.
Published: (2024)

Inference on Common Trends in a Cointegrated Nonlinear SVAR
by: Duffy, James A., et al.
Published: (2025)

Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods
by: Li, Housen, et al.
Published: (2017)

Kernel Density Estimation and Convolution Revisited
by: Tenkorang, Nicholas, et al.
Published: (2025)

Multigrid Monte Carlo Revisited: Theory and Bayesian Inference
by: Kazashi, Yoshihito, et al.
Published: (2024)

Early stopping for conjugate gradients in statistical inverse problems
by: Hucker, Laura, et al.
Published: (2024)

Importance sampling for Sobol' indices estimation
by: Boucharif, Haythem, et al.
Published: (2025)

Kernel Density Machines
by: Della Vecchia, Andrea, et al.
Published: (2025)

Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
by: Rastogi, Abhishake, et al.
Published: (2019)

Convergence rates of non-stationary and deep Gaussian process regression
by: Osborne, Conor, et al.
Published: (2023)

Least squares approximations in linear statistical inverse learning problems
by: Helin, Tapio
Published: (2022)

Maximum a posteriori testing in statistical inverse problems
by: Kretschmann, Remo, et al.
Published: (2024)

Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations
by: Jia, Junxiong, et al.
Published: (2022)

Frame-constrained Total Variation Regularization for White Noise Regression
by: del Álamo, Miguel, et al.
Published: (2018)

Bootstrap Consistency for Empirical Likelihood in Density Ratio Models
by: Zhuang, Weiwei, et al.
Published: (2025)

Conditional Aalen--Johansen estimation
by: Bladt, Martin, et al.
Published: (2023)

Spatial Proportional Hazards Model with Differential Regularization
by: Tedesco, Lorenzo, et al.
Published: (2024)

A Scalable Sequential Framework for Dynamic Inverse Problems via Model Parameter Estimation
by: Keating, Aryeh, et al.
Published: (2026)

Average Marginal Effects in One-Step Partially Linear Instrumental Regressions
by: Girard, Lucas, et al.
Published: (2026)

Parameter Estimation in Nonlinear Multivariate Stochastic Differential Equations Based on Splitting Schemes
by: Pilipovic, Predrag, et al.
Published: (2022)

Stein variational gradient descent on infinite-dimensional space and applications to statistical inverse problems
by: Jia, Junxiong, et al.
Published: (2021)

New Risk Bounds for 2D Total Variation Denoising
by: Chatterjee, Sabyasachi, et al.
Published: (2019)

TwinKernel Estimation for Point Process Intensity Functions: Adaptive Nonparametric Methods via Orbital Regularity
by: Nembé, Jocelyn
Published: (2025)

Quasi-Monte Carlo and importance sampling methods for Bayesian inverse problems
by: He, Zhijian, et al.
Published: (2024)

Estimation and Hypothesis Testing of Derivatives in Smoothing Spline ANOVA Models
by: Liu, Ruiqi, et al.
Published: (2023)

On Fisher Consistency of Surrogate Losses for Optimal Dynamic Treatment Regimes with Multiple Categorical Treatments per Stage
by: Laha, Nilanjana, et al.
Published: (2025)

Inverse learning in Hilbert scales
by: Rastogi, Abhishake, et al.
Published: (2020)

The Cost of Adaptation under Differential Privacy: Optimal Adaptive Federated Density Estimation
by: Cai, T. Tony, et al.
Published: (2025)

Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
by: Yu, Xinyue, et al.
Published: (2025)

Model-free filtering in high dimensions via projection and score-based diffusions
by: Christensen, Sören, et al.
Published: (2025)

Nonparametric Inference on Dose-Response Curves Without the Positivity Condition
by: Zhang, Yikun, et al.
Published: (2024)

Asymptotically efficient estimation under local constraint in Wicksell's problem
by: Gili, Francesco, et al.
Published: (2024)