Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods

DOI10.1137/21M1410853zbMath1493.62124arXiv2104.03384OpenAlexW3148409799MaRDI QIDQ5090110

Oliver R. A. Dunbar, Marie-Therese Wolfram, Andrew B. Duncan, Andrew M. Stuart

Publication date: 15 July 2022

Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.03384

zbMATH Keywords

multiscale analysisensemble methodsGaussian process regressionLangevin samplingensemble Kalman sampler

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21)

Related Items (1)

Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data

Cites Work

• Unnamed Item
• The pseudo-marginal approach for efficient Monte Carlo computations
• Ensemble samplers with affine invariance
• Mean field limits for interacting diffusions in a two-scale potential
• Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems
• Rapid mixing for the Lorenz attractor and statistical limit laws for their time-1 maps
• Exponential convergence of Langevin distributions and their discrete approximations
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Ensemble preconditioning for Markov chain Monte Carlo simulation
• Statistical and computational inverse problems.
• Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection
• Sampling from rough energy landscapes
• Calibrate, emulate, sample
• Discrete gradients for computational Bayesian inference
• Monte Carlo strategies in scientific computing.
• Numerical methods for multiscale inverse problems
• Bayesian Calibration of Computer Models
• Delayed rejection in reversible jump Metropolis-Hastings
• Ensemble Kalman methods for inverse problems
• Pathwise accuracy and ergodicity of metropolized integrators for SDEs
• Deterministic Nonperiodic Flow
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Combining Field Data and Computer Simulations for Calibration and Prediction
• Extracting macroscopic dynamics: model problems and algorithms
• Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
• Affine Invariant Interacting Langevin Dynamics for Bayesian Inference
• Wasserstein stability estimates for covariance-preconditioned Fokker–Planck equations
• Fokker--Planck Particle Systems for Bayesian Inference: Computational Approaches
• Derivative-Free Bayesian Inversion Using Multiscale Dynamics
• Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler
• Mean Field Limits for Interacting Diffusions with Colored Noise: Phase Transitions and Spectral Numerical Methods
• An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems
• Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
• Data assimilation: The Schrödinger perspective
• Parameter Estimation for Macroscopic Pedestrian Dynamics Models from Microscopic Data
• Multiscale Methods
• Comparing dynamics: deep neural networks versus glassy systems
• MCMC methods for functions: modifying old algorithms to make them faster

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