Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors

DOI10.1137/130929904zbMath1322.65008arXiv1302.2213OpenAlexW2963018747MaRDI QIDQ2945165

Sebastian J. Vollmer

Publication date: 9 September 2015

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1302.2213

zbMATH Keywords

computational complexitygroundwater flowMetropolis-Hastings algorithmMarkov chain Monte Carlo methodsBayesian inverse problemselliptic inverse problemnon-Gaussian prior measures

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Probabilistic methods, particle methods, etc. for boundary value problems involving PDEs (65N75) Monte Carlo methods (65C05) Flows in porous media; filtration; seepage (76S05) Inverse problems for PDEs (35R30) Numerical analysis or methods applied to Markov chains (65C40) Complexity and performance of numerical algorithms (65Y20) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21)

Related Items (8)

Efficient parameter estimation for a methane hydrate model with active subspaces ⋮ Optimization based methods for partially observed chaotic systems ⋮ On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems ⋮ Dimension‐independent Markov chain Monte Carlo on the sphere ⋮ A Bayesian Approach to Estimating Background Flows from a Passive Scalar ⋮ MALA-within-Gibbs Samplers for High-Dimensional Distributions with Sparse Conditional Structure ⋮ Nonlocal TV-Gaussian prior for Bayesian inverse problems with applications to limited CT reconstruction ⋮ Accelerated Dimension-Independent Adaptive Metropolis

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