Pages that link to "Item:Q1685436"
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The following pages link to Geometric MCMC for infinite-dimensional inverse problems (Q1685436):
Displaying 50 items.
- Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems (Q729447) (← links)
- The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (Q2022037) (← links)
- Bayesian inversion of a diffusion model with application to biology (Q2040282) (← links)
- Ensemble sampler for infinite-dimensional inverse problems (Q2058734) (← links)
- Generalized parallel tempering on Bayesian inverse problems (Q2058888) (← links)
- Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors (Q2073706) (← links)
- Variational inference for nonlinear inverse problems via neural net kernels: comparison to Bayesian neural networks, application to topology optimization (Q2083125) (← links)
- Mixing rates for Hamiltonian Monte Carlo algorithms in finite and infinite dimensions (Q2093317) (← links)
- Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics (Q2123822) (← links)
- Physics-informed machine learning with conditional Karhunen-Loève expansions (Q2126979) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset (Q2209715) (← links)
- Adaptive dimension reduction to accelerate infinite-dimensional geometric Markov chain Monte Carlo (Q2221416) (← links)
- Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification (Q2222401) (← links)
- Bayesian inference of heterogeneous epidemic models: application to COVID-19 spread accounting for long-term care facilities (Q2237746) (← links)
- Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo (Q2242166) (← links)
- Proposals which speed up function-space MCMC (Q2252357) (← links)
- Non-stationary phase of the MALA algorithm (Q2315120) (← links)
- Bayesian neural network priors for edge-preserving inversion (Q2674903) (← links)
- A unified performance analysis of likelihood-informed subspace methods (Q2676941) (← links)
- Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors (Q2945165) (← links)
- Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals (Q3176244) (← links)
- Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation (Q4960588) (← links)
- Bernstein--von Mises Theorems and Uncertainty Quantification for Linear Inverse Problems (Q4960994) (← links)
- Multilevel Markov Chain Monte Carlo for Bayesian Inversion of Parabolic Partial Differential Equations under Gaussian Prior (Q4995109) (← links)
- Stein Variational Reduced Basis Bayesian Inversion (Q4997362) (← links)
- Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo (Q4997424) (← links)
- Analysis of a multilevel Markov chain Monte Carlo finite element method for Bayesian inversion of log-normal diffusions (Q5000562) (← links)
- Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem (Q5062121) (← links)
- An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks (Q5079536) (← links)
- Scalable Optimization-Based Sampling on Function Space (Q5112552) (← links)
- Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem (Q5117388) (← links)
- A Bayesian Approach to Estimating Background Flows from a Passive Scalar (Q5119638) (← links)
- Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs (Q5132022) (← links)
- Non-stationary multi-layered Gaussian priors for Bayesian inversion (Q5150818) (← links)
- Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion (Q5158929) (← links)
- Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo (Q5161745) (← links)
- Data assimilation: The Schrödinger perspective (Q5230525) (← links)
- Two Metropolis--Hastings Algorithms for Posterior Measures with Non-Gaussian Priors in Infinite Dimensions (Q5237191) (← links)
- Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review (Q5854065) (← links)
- Data-free likelihood-informed dimension reduction of Bayesian inverse problems (Q5859742) (← links)
- Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference (Q5880609) (← links)
- Statistical Finite Elements via Langevin Dynamics (Q5880613) (← links)
- Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
- Localization of Moving Sources: Uniqueness, Stability, and Bayesian Inference (Q6100169) (← links)
- Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143) (← links)
- Non-reversible guided Metropolis kernel (Q6116753) (← links)
- Laplace priors and spatial inhomogeneity in Bayesian inverse problems (Q6120819) (← links)
- Non-centered parametric variational Bayes’ approach for hierarchical inverse problems of partial differential equations (Q6129005) (← links)
- On the accept-reject mechanism for Metropolis-Hastings algorithms (Q6139681) (← links)
