Computer Model Calibration Using High-Dimensional Output

By D. Higdon, James R. Gattiker, Brett Williams, M. Rightley. 2008

977 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

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Citations in the corpus

Kernels for Vector-Valued Functions: a Review (2011, methodology): “Examples include [42] and [23] under the LMC framework or [14] and [99] under the process convolution approach.”
Efficient Emulators for Multivariate Deterministic Functions (2008, background): “…Institute of Mathematical Statistics, and Interface Foundation of North America Journal of Computational and Graphical Statistics, Volume 17, Number 4, Pages 827–843 DOI: 10.1198/106186008X384032 827 2001; Bayarriet al. 2005; Conti and O’Hagan 2007; Higdon et al. 2008; Bayarriet al. 2007).”
Learning about physical parameters: the importance of model discrepancy (2014, methodology): “Since then, their inferential ideas and modeling framework have been widely adopted and further developed, e.g. Higdon et al (2004, 2008), Qian and Wu (2008), Goldstein and Rougier (2009), Bayarri et al (2007), Gramacy and Lee (2008).”
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification (2013, background): “[16,17] model non-stationary responses, while [18,15] (in quite different ways) attempt to capture correlations between multiple outputs.”
THE COYOTE UNIVERSE. II. COSMOLOGICAL MODELS AND PRECISION EMULATION OF THE NONLINEAR MATTER POWER SPECTRUM (2009, methodology): “An alternative is to use a Bayesian approach as detailed in Habib et al. (2007) or, more generally in Higdon et al. (2008), to find the optimal parameters of the GP.”
Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data (2009, background): “See also Higdon, Gattiker, and Willians (2008) for a related approach.”
A Bayesian approach for parameter estimation and prediction using a computationally intensive model (2014, methodology): “Separate Gaussian processes models [6, 22] are used to model each of the weight functions; this is described in the following two subsections.”
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion (2012, methodology): “One may also approximate the parameter-to-observable map with a Gaussian process model that is constructed via Bayesian model calibration over a limited set of training data (limited in both the number of experimental observations available as well as the number of forward model evaluations) [23, 28”