LDDMM distances are introduced as an interpretable metric for Bayesian calibration of infinite-dimensional computer model outputs, supporting predictive posteriors on shapes via RKHS representations.
Riemann manifold langevin and hamiltonian monte carlo methods
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Diffeomorphic registration distances for Bayesian calibration of infinite-dimensional computer models
LDDMM distances are introduced as an interpretable metric for Bayesian calibration of infinite-dimensional computer model outputs, supporting predictive posteriors on shapes via RKHS representations.