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Found 6 papers, 3 papers with code
Transport map unadjusted Langevin algorithms: learning and discretizing perturbed samplers
We show that in continuous time, when a transport map is applied to Langevin dynamics, the result is a Riemannian manifold Langevin dynamics (RMLD) with metric defined by the transport map.
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport
We tackle this challenging Bayesian inference problem using a likelihood-free approach based on measure transport together with the construction of summary statistics for the image data.
Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics
We introduce a novel geometry-informed irreversible perturbation that accelerates convergence of the Langevin algorithm for Bayesian computation.
Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation
Although some existing parametric distribution families are designed to perform efficiently in high dimensions, their applicability within the cross-entropy method is limited to problems with dimension of O(1e2).
Computation
A layered multiple importance sampling scheme for focused optimal Bayesian experimental design
We develop a new computational approach for "focused" optimal Bayesian experimental design with nonlinear models, with the goal of maximizing expected information gain in targeted subsets of model parameters.
Computation Methodology
Sequential Bayesian optimal experimental design via approximate dynamic programming
Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.
