TIPreL uses a time- and position-dependent preconditioner in Langevin dynamics to address both global mode coverage and local exploration, with convergence proven in Wasserstein-2 distance under extended conditions.
Wilcox, Carsten Burstedde, and Omar Ghattas
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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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Time-Inhomogeneous Preconditioned Langevin Dynamics
TIPreL uses a time- and position-dependent preconditioner in Langevin dynamics to address both global mode coverage and local exploration, with convergence proven in Wasserstein-2 distance under extended conditions.
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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.