Iterative construction of Gaussian process surrogate models for Bayesian inference-Reference-Cited by-同舟云学术

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Iterative construction of Gaussian process surrogate models for Bayesian inference

Published:2020-07 Issue: Volume:207 Page:55-72
ISSN:0378-3758
Container-title:Journal of Statistical Planning and Inference
language:en
Short-container-title:Journal of Statistical Planning and Inference

Author:

Alawieh Leen,Goodman Jonathan,Bell John B.

Funder

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference75 articles.

1. Sampling the posterior: An approach to non-gaussian data assimilation;Apte;Physica D,2007

2. Approximation errors and model reduction with an application in optical diffusion tomography;Arridge;Inverse Problems,2006

3. Markov chain monte carlo-based approaches for inference in computationally intensive inverse problems;Berger,2003

4. Multi-output separable gaussian process: Towards an efficient, fully bayesian paradigm for uncertainty quantification;Bilionis;J. Comput. Phys.,2013

5. A bayesian approach to model inadequacy for polynomial regression;Blight;Biometrika,1975

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