Solving Bayesian inverse problems from the perspective of deep generative networks
- 19 June 2019
- journal article
- research article
- Published by Springer Nature in Computational Mechanics
- Vol. 64 (2) , 395-408
Abstract
No abstract availableKeywords
Funding Information
- Division of Mathematical Sciences (1613861)
This publication has 19 references indexed in Scilit:
- Dimension-independent likelihood-informed MCMCJournal of Computational Physics, 2016
- MAP estimators and their consistency in Bayesian nonparametric inverse problemsInverse Problems, 2013
- MCMC Methods for Functions: Modifying Old Algorithms to Make Them FasterStatistical Science, 2013
- Multi-scale computational homogenization: Trends and challengesJournal of Computational and Applied Mathematics, 2010
- VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITYAnalysis and Applications, 2010
- MCMC METHODS FOR DIFFUSION BRIDGESStochastics and Dynamics, 2008
- The Ensemble Kalman Filter: theoretical formulation and practical implementationOcean Dynamics, 2003
- Solution of inverse problems in elasticity imaging using the adjoint methodInverse Problems, 2003
- Online Model Selection Based on the Variational BayesNeural Computation, 2001
- Hybrid Monte CarloPhysics Letters B, 1987