New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
SIAM, Philadelphia (2018)
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This is an introductory review of the linear algebraic subproblems and contemporary solvers in variational data assimilation for geophysical applications.
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Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
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An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
This is an introductory review of the linear algebraic subproblems and contemporary solvers in variational data assimilation for geophysical applications.