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Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors
Taewon Cho
, Julianne Chung
,
Jiahua Jiang
Mathematics
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peer-review
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Dive into the research topics of 'Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors'. Together they form a unique fingerprint.
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Keyphrases
Covariance Matrix
100%
Gaussian Prior
100%
Large-scale Inverse Problems
100%
Mixed Gaussian
100%
Hybrid Projection Method
100%
Regularization Parameter
33%
Distinctive Features
16%
Training Data
16%
Mixing Parameters
16%
Hybrid Method
16%
Iterative Process
16%
Numerical Examples
16%
Iterative Methods
16%
Inverse Problem
16%
Tomographic Reconstruction
16%
Positive Definite
16%
Data Assimilation
16%
Mean Vector
16%
Solution Process
16%
Ill-posed Inverse Problem
16%
Weight Parameter
16%
Convex Combination of Matrices
16%
Regularized Problem
16%
Covariance Kernel
16%
Bidiagonalization
16%
Iterative Projection Algorithms
16%
Mathematics
Gaussian Distribution
100%
Covariance Matrix
100%
Projection Method
100%
Regularization
33%
Matrix (Mathematics)
16%
Covariance
16%
Iterative Process
16%
Training Data
16%
Numerical Example
16%
Iterative Method
16%
Positive Definite
16%
Convex Combination
16%
Good Choice
16%