Pages that link to "Item:Q2374891"

The following pages link to Dimension-independent likelihood-informed MCMC (Q2374891):

Displaying 50 items.

• Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems (Q4689166) (← links)
• Data-driven forward discretizations for Bayesian inversion (Q4970560) (← links)
• Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo (Q4997424) (← links)
• Probabilistic parameter estimation in a 2-step chemical kinetics model for <i>n</i>-dodecane jet autoignition (Q5032141) (← links)
• A Bayesian method for an inverse transmission scattering problem in acoustics (Q5036815) (← links)
• Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems (Q5044972) (← links)
• Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy (Q5052906) (← links)
• Certified dimension reduction in nonlinear Bayesian inverse problems (Q5082037) (← links)
• Stein Variational Gradient Descent on Infinite-Dimensional Space and Applications to Statistical Inverse Problems (Q5102236) (← links)
• Scalable Optimization-Based Sampling on Function Space (Q5112552) (← links)
• Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs (Q5132022) (← links)
• Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors (Q5146679) (← links)
• Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems (Q5149778) (← links)
• Non-stationary multi-layered Gaussian priors for Bayesian inversion (Q5150818) (← links)
• Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo (Q5161745) (← links)
• Pass-Efficient Randomized Algorithms for Low-Rank Matrix Approximation Using Any Number of Views (Q5194597) (← links)
• Multilevel Markov Chain Monte Carlo (Q5232352) (← links)
• Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems (Q5237188) (← links)
• Two Metropolis--Hastings Algorithms for Posterior Measures with Non-Gaussian Priors in Infinite Dimensions (Q5237191) (← 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)
• Particle Filtering for Stochastic Navier--Stokes Signal Observed with Linear Additive Noise (Q5745136) (← links)
• Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review (Q5854065) (← links)
• Variational Bayes' Method for Functions with Applications to Some Inverse Problems (Q5857841) (← links)
• Data-free likelihood-informed dimension reduction of Bayesian inverse problems (Q5859742) (← links)
• Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference (Q5880609) (← links)
• Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
• Properties of the affine‐invariant ensemble sampler's ‘stretch move’ in high dimensions (Q6075139) (← links)
• Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143) (← links)
• Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings (Q6109156) (← links)
• Laplace priors and spatial inhomogeneity in Bayesian inverse problems (Q6120819) (← links)
• Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty (Q6147033) (← 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)
• A Bayesian approach for consistent reconstruction of inclusions (Q6149891) (← links)
• Online MCMC Thinning with Kernelized Stein Discrepancy (Q6151668) (← links)
• Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)
• On unifying randomized methods for inverse problems (Q6162746) (← links)
• Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems (Q6194963) (← 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)
• Consistent inference for diffusions from low frequency measurements (Q6550965) (← links)
• A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems (Q6555345) (← links)
• Adaptive inference over Besov spaces in the white noise model using \(p\)-exponential priors (Q6565322) (← links)
• Conditional sampling with monotone GANs: from generative models to likelihood-free inference (Q6587628) (← links)
• Principal feature detection via \(\phi \)-Sobolev inequalities (Q6589581) (← links)
• Optimal experimental design: formulations and computations (Q6598420) (← links)
• hIPPYlib-MUQ: a Bayesian inference software framework for integration of data with complex predictive models under uncertainty (Q6601373) (← links)
• On posterior consistency of data assimilation with Gaussian process priors: the 2D-Navier-Stokes equations (Q6621548) (← links)
• Optimal neural network approximation of Wasserstein gradient direction via convex optimization (Q6633046) (← links)