The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Stochastic spectral methods for efficient Bayesian solution of inverse problems
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Citations
Cites
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Inverse Problem Theory and Methods for Model Parameter Estimation
Tarantola, A. (2005) · doi:10.1137/1.9780898717921 · Cited by 6,585
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Practical markov chain monte carlo
Geyer, C. J. (1992) · Statistical Science · doi:10.1214/ss/1177011137 · Cited by 2,045
Cited by
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718
Simulation-based optimal bayesian experimental design for nonlinear systems
Huan, X., & Marzouk, Y. M. (2013) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.08.013 · Cited by 438
“, [56, 57, 58]), and this is the approach we shall use.”
BackgroundMethodSelf-citation
Metadata
- Authors
- Marzouk, Y. M., Najm, H. N., & Rahn, L. A.
- Venue
- Journal of Computational Physics
- Publisher
- Elsevier BV
- Published
- 2007-06
- Volume
- 224
- Issue
- 2
- Pages
- 560-586
- ISSN
- 0021-9991 (ISSN-L)
- OpenAlex
- W2017880874
- Cited by
- 470
- References
- 4
- FWCI
- 6.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Marzouk, Y. M., Najm, H. N., & Rahn, L. A. (2007). Stochastic spectral methods for efficient Bayesian solution of inverse problems. Journal of Computational Physics. https://doi.org/10.1016/j.jcp.2006.10.010
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
