Cover Image

Consistency of variational inference for Besov priors in non-linear inverse problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zu, Shaokang, Jia, Junxiong, Wang, Zhiguo
Format:	Preprint
Published:	2025
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 Nonlinear Inverse Problems of Partial Differential Equations
by: Zu, Shaokang, et al.
Published: (2024)

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

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

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

Adaptive inference over Besov spaces in the white noise model using $p$-exponential priors
by: Agapiou, Sergios, et al.
Published: (2022)

Random tree Besov priors: Data-driven regularisation parameter selection
by: Kekkonen, Hanne, et al.
Published: (2026)

Linear methods for non-linear inverse problems
by: Koers, Geerten, et al.
Published: (2024)

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

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

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

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

Designing truncated priors for direct and inverse Bayesian problems
by: Agapiou, Sergios, et al.
Published: (2021)

Nonparametric Bayesian inference for stochastic processes with piecewise constant priors
by: Belomestny, Denis, et al.
Published: (2023)

Adaptive Gaussian Process Regression for Bayesian inverse problems
by: Villani, Paolo, et al.
Published: (2024)

Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems
by: Rastogi, Abhishake
Published: (2020)

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

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

Empirical tail dependence functions in high dimensions: uniform linearizations and inference
by: Bücher, Axel, 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)

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

Information bounds for inference in stochastic evolution equations observed under noise
by: Pasemann, Gregor, et al.
Published: (2025)

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

Optimal regularized hypothesis testing in statistical inverse problems
by: Kretschmann, Remo, et al.
Published: (2022)

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

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

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

Non-centered parametric variational Bayes' approach for hierarchical inverse problems of partial differential equations
by: Sui, Jiaming, et al.
Published: (2022)

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

Semiparametric Bernstein-von Mises Phenomenon via Isotonized Posterior in Wicksell's problem
by: Gili, Francesco, et al.
Published: (2025)

Robust inference for intermittently-monitored step-stress tests under Weibull lifetime distributions
by: Balakrishnan, Narayanaswamy, et al.
Published: (2022)

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

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

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)

A uniform kernel trick for high-dimensional two-sample problems
by: Cárcamo, Javier, et al.
Published: (2022)

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

Statistical inference for rough volatility: Central limit theorems
by: Chong, Carsten, et al.
Published: (2022)

Estimation in linear high dimensional Hawkes processes: a Bayesian approach
by: Rousseau, Judith, et al.
Published: (2025)