An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

DOI10.1016/j.jcp.2015.03.047zbMath1349.62025OpenAlexW2043895979MaRDI QIDQ349984

Weixuan Li, Guang Lin

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2015.03.047

zbMATH Keywords

Gaussian mixtureadaptive samplinguncertainty reductioninverse modelingmixture of polynomial chaos expansions

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05)

Related Items (7)

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN ⋮ Efficient estimation of hydraulic conductivity heterogeneity with non-redundant measurement information ⋮ On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models ⋮ Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics ⋮ Constrained iterative ensemble smoother for multi solution search assisted history matching ⋮ A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness ⋮ An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

Uses Software

• AS 136
• PRMLT

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling
• Identification of multi-modal random variables through mixtures of polynomial chaos expansions
• Stochastic spectral methods for efficient Bayesian solution of inverse problems
• On the convergence properties of the EM algorithm
• Statistical and computational inverse problems.
• Modeling uncertainty in flow simulations via generalized polynomial chaos.
• An adaptive multi-element generalized polynomial chaos method for stochastic differential equations
• Uncertainty quantification for porous media flows
• Bayesian Calibration of Computer Models
• Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
• Inverse problems: A Bayesian perspective
• Combining Field Data and Computer Simulations for Calibration and Prediction

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