A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems-Reference-Cited by-同舟云学术

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A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems

Published:2023-06-16 Issue:4 Volume:33 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Li Sijing,Zhang Cheng,Zhang Zhiwen^ORCID,Zhao Hongkai

Funder

Division of Mathematical Sciences

Hong Kong RGC

National Natural Science Foundation of China

HKU

Peking University

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-023-10262-y.pdf

Reference47 articles.

1. Abdulle, A., Barth, A., Schwab, C.: Multilevel Monte Carlo methods for stochastic elliptic multiscale PDEs. Multiscale Model. Simul. 11, 1033–1070 (2013)

2. Asokan, B.V., Zabaras, N.: A stochastic variational multiscale method for diffusion in heterogeneous random media. J. Comput. Phys. 218, 654–676 (2006)

3. Babuska, I., Nobile, F., Tempone, R.: A stochastic collocation method for elliptic partial differential equations with random input data. SIAM J. Numer. Anal. 45, 1005–1034 (2007)

4. Babuska, I., Tempone, R., Zouraris, G.: Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J. Numer. Anal. 42, 800–825 (2004)

5. Bachmayr, M., Cohen, A., DeVore, R., Migliorati, G.: Sparse polynomial approximation of parametric elliptic PDEs, Part II: lognormal coefficients. ESAIM: Math. Model. Numer. Anal. 51, 341–363 (2017)

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