Pages that link to "Item:Q2909273"

The following pages link to A stochastic Newton MCMC method for large-scale statistical inverse problems with application to seismic inversion (Q2909273):

Displaying 44 items.

• Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo (Q5161745) (← links)
• Robust Optimization of PDEs with Random Coefficients Using a Multilevel Monte Carlo Method (Q5228353) (← links)
• <i>A priori</i> estimates of attraction basins for nonlinear least squares, with application to Helmholtz seismic inverse problem (Q5236700) (← links)
• Transform-based particle filtering for elliptic Bayesian inverse problems (Q5236701) (← links)
• Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems (Q5237188) (← links)
• Bayesian Inverse Problems and Kalman Filters (Q5256557) (← links)
• Metropolized Randomized Maximum Likelihood for Improved Sampling from Multimodal Distributions (Q5269863) (← links)
• Sequential Implicit Sampling Methods for Bayesian Inverse Problems (Q5269874) (← links)
• A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem (Q5348010) (← links)
• Bayesian Inverse Problems with $l_1$ Priors: A Randomize-Then-Optimize Approach (Q5372623) (← links)
• Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems (Q5372624) (← links)
• Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models (Q5741184) (← links)
• Variational Bayes' Method for Functions with Applications to Some Inverse Problems (Q5857841) (← links)
• Statistical Finite Elements via Langevin Dynamics (Q5880613) (← links)
• Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
• VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
• A Benchmark for the Bayesian Inversion of Coefficients in Partial Differential Equations (Q6071826) (← links)
• Gradient-based adaptive importance samplers (Q6078205) (← links)
• Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions (Q6090288) (← links)
• Bayesian inversion of log-normal eikonal equations (Q6101034) (← links)
• A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (Q6109162) (← links)
• Multiscale sampling for the inverse modeling of partial differential equations (Q6119240) (← links)
• Laplace priors and spatial inhomogeneity in Bayesian inverse problems (Q6120819) (← links)
• Stochastic elliptic inverse problems. Solvability, convergence rates, discretization, and applications (Q6137266) (← links)
• On the accept-reject mechanism for Metropolis-Hastings algorithms (Q6139681) (← links)
• Reduced-order model-based variational inference with normalizing flows for Bayesian elliptic inverse problems (Q6145183) (← links)
• Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
• Solving linear Bayesian inverse problems using a fractional total variation-Gaussian (FTG) prior and transport map (Q6148394) (← links)
• Online MCMC Thinning with Kernelized Stein Discrepancy (Q6151668) (← links)
• Horseshoe Priors for Edge-Preserving Linear Bayesian Inversion (Q6156655) (← links)
• On unifying randomized methods for inverse problems (Q6162746) (← links)
• A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems (Q6172145) (← links)
• Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems (Q6194963) (← links)
• Kernel methods are competitive for operator learning (Q6202132) (← links)
• Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135) (← links)
• A computational framework for infinite-dimensional Bayesian inverse problems. II: stochastic Newton MCMC with application to ice sheet flow inverse problems (Q6486743) (← links)
• Adjoint Hamiltonian Monte Carlo algorithm for the estimation of elastic modulus through the inversion of elastic wave propagation data (Q6497705) (← links)
• A new bi-fidelity model reduction method for Bayesian inverse problems (Q6553850) (← links)
• A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems (Q6555345) (← links)
• Certified coordinate selection for high-dimensional Bayesian inversion with Laplace prior (Q6581669) (← links)
• hIPPYlib-MUQ: a Bayesian inference software framework for integration of data with complex predictive models under uncertainty (Q6601373) (← links)
• On variational inference and maximum likelihood estimation with the \(\lambda\)-exponential family (Q6620121) (← links)
• Adaptive stepsize algorithms for Langevin dynamics (Q6638211) (← links)
• Covariance-modulated optimal transport and gradient flows (Q6660122) (← links)