Pages that link to "Item:Q695159"

The following pages link to Bayesian inference with optimal maps (Q695159):

Displaying 37 items.

• Fast $L^2$ Optimal Mass Transport via Reduced Basis Methods for the Monge--Ampère Equation (Q5048577) (← links)
• (Q5053263) (← links)
• Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics (Q5097837) (← links)
• Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler (Q5109769) (← links)
• Diffusion Map-based Algorithm for Gain Function Approximation in the Feedback Particle Filter (Q5119640) (← links)
• Transport Map Accelerated Adaptive Importance Sampling, and Application to Inverse Problems Arising from Multiscale Stochastic Reaction Networks (Q5139357) (← links)
• A Distributed Framework for the Construction of Transport Maps (Q5154132) (← links)
• Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion (Q5158929) (← links)
• A Minimum Free Energy Model of Motor Learning (Q5214382) (← links)
• Ensemble Transport Adaptive Importance Sampling (Q5228364) (← links)
• Data assimilation: The Schrödinger perspective (Q5230525) (← links)
• A Guided Sequential Monte Carlo Method for the Assimilation of Data into Stochastic Dynamical Systems (Q5253368) (← links)
• Bayesian Inverse Problems and Kalman Filters (Q5256557) (← links)
• Bayesian Inverse Problems with $l_1$ Priors: A Randomize-Then-Optimize Approach (Q5372623) (← links)
• Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems (Q5372624) (← links)
• Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
• Bayesian inference via projections (Q5963777) (← links)
• Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models (Q6090720) (← links)
• Adaptive mesh methods on compact manifolds via optimal transport and optimal information transport (Q6119276) (← links)
• Ensemble transport smoothing. I: Unified framework (Q6145480) (← links)
• Solving linear Bayesian inverse problems using a fractional total variation-Gaussian (FTG) prior and transport map (Q6148394) (← links)
• Horseshoe Priors for Edge-Preserving Linear Bayesian Inversion (Q6156655) (← links)
• A Continuation Method in Bayesian Inference (Q6164173) (← links)
• Bayesian learning with Wasserstein barycenters (Q6175889) (← links)
• Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy (Q6185714) (← links)
• Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events (Q6189161) (← links)
• A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows (Q6194145) (← links)
• Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate (Q6198152) (← links)
• Transport map sampling with PGD model reduction for fast dynamical Bayesian data assimilation (Q6549924) (← links)
• Learning to solve Bayesian inverse problems: an amortized variational inference approach using Gaussian and flow guides (Q6560691) (← links)
• Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields (Q6567936) (← links)
• Conditional sampling with monotone GANs: from generative models to likelihood-free inference (Q6587628) (← links)
• Optimal experimental design: formulations and computations (Q6598420) (← links)
• The transport map computed by iterated function system (Q6633197) (← links)
• Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields (Q6655987) (← links)
• On the representation and learning of monotone triangular transport maps (Q6659499) (← links)
• Variational Bayesian optimal experimental design with normalizing flows (Q6663253) (← links)