An iterative version of the adaptive Gaussian mixture filter

DOI10.1007/S10596-014-9402-6zbMath1378.86012OpenAlexW2015626270MaRDI QIDQ680289

Rolf J. Lorentzen, Andreas S. Stordal

Publication date: 22 January 2018

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-014-9402-6

zbMATH Keywords

data assimilationensemble Kalman filterhistory matchingGaussian mixture filtersiterative importance sampling

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Sampling theory, sample surveys (62D05) Geostatistics (86A32) Geological problems (86A60)

Related Items (5)

Bridging deep convolutional autoencoders and ensemble smoothers for improved estimation of channelized reservoirs ⋮ Iterative Bayesian inversion with Gaussian mixtures: finite sample implementation and large sample asymptotics ⋮ A modified randomized maximum likelihood for improved Bayesian history matching ⋮ Complex geology estimation using the iterative adaptive Gaussian mixture filter ⋮ Bridging multipoint statistics and truncated Gaussian fields for improved estimation of channelized reservoirs with ensemble methods

Cites Work

• Filtering with state space localized Kalman gain
• Bridging the ensemble Kalman filter and particle filters: The adaptive Gaussian mixture filter
• Sequential Monte Carlo Methods in Practice
• Local Adaptive Importance Sampling for Multivariate Densities With Strong Nonlinear Relationships
• Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
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