Sequential Kalman tuning of the \(t\)-preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems
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Publication:6654157
DOI10.1088/1361-6420/ad934bMaRDI QIDQ6654157
Uroš Seljak, Richard D. P. Grumitt, Minas Karamanis
Publication date: 18 December 2024
Published in: Inverse Problems (Search for Journal in Brave)
Cites Work
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- Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- On the convergence of adaptive sequential Monte Carlo methods
- On adaptive resampling strategies for sequential Monte Carlo methods
- Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
- On the convergence of the ensemble Kalman filter.
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Optimal scaling for various Metropolis-Hastings algorithms.
- Ensemble preconditioning for Markov chain Monte Carlo simulation
- Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution
- Statistical and computational inverse problems.
- Ensemble slice sampling. Parallel, black-box and gradient-free inference for correlated \& multimodal distributions
- Ensemble Kalman inversion for nonlinear problems: weights, consistency, and variance bounds
- Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference
- Hybrid multi-step estimators for stochastic differential equations based on sampled data
- Optimal tuning of the hybrid Monte Carlo algorithm
- Ensemble Kalman inversion for general likelihoods
- Iterated Kalman methodology for inverse problems
- A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems
- Ensemble Kalman methods for inverse problems
- Practical Markov Chain Monte Carlo
- Sequential Monte Carlo Samplers
- Handbook of Markov Chain Monte Carlo
- A sequential particle filter method for static models
- Parameterizations for ensemble Kalman inversion
- Bayesian inversion in resin transfer molding
- Ergodicity of Markov chain Monte Carlo with reversible proposal
- Efficient derivative-free Bayesian inference for large-scale inverse problems
- Convergence acceleration of ensemble Kalman inversion in nonlinear settings
- Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler
- Tikhonov Regularization within Ensemble Kalman Inversion
- Adaptive regularisation for ensemble Kalman inversion
- Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
- Analysis of the Ensemble Kalman Filter for Inverse Problems
- The ensemble Kalman filter for combined state and parameter estimation
- Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
- Waste-Free Sequential Monte Carlo
- Data Assimilation
- MCMC methods for functions: modifying old algorithms to make them faster
- Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
- Ensemble MCMC: accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter
- Multilevel Delayed Acceptance MCMC
- Adaptive tuning of Hamiltonian Monte Carlo within sequential Monte Carlo
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