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Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks

, , and . (2021)cite arxiv:2101.03906.

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Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks, , and . (2021)cite arxiv:2101.03906.Bayesian spatiotemporal modeling for inverse problems., , and . Stat. Comput., 33 (4): 89 (August 2023)Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse Problems., , , and . J. Comput. Phys., (2016)Spherical Hamiltonian Monte Carlo for Constrained Target Distributions., , and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 629-637. JMLR.org, (2014)Wormhole Hamiltonian Monte Carlo., , and . AAAI, page 1953-1959. AAAI Press, (2014)Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models.. J. Mach. Learn. Res., (2022)Calibrate, emulate, sample., , , , and . J. Comput. Phys., (2021)An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics., , , , and . Bioinform., 31 (20): 3282-3289 (2015)Nonparametric Fisher Geometry with Application to Density Estimation., , , and . UAI, volume 124 of Proceedings of Machine Learning Research, page 101-110. AUAI Press, (2020)Adaptive dimension reduction to accelerate infinite-dimensional geometric Markov Chain Monte Carlo.. J. Comput. Phys., (2019)