Bayesian analysis of computer code outputs: A tutorial

By Anthony OʼHagan. 2006

842 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Some fields also refer to the surrogate modeling as function approximation, meta-modeling, response surface method, or model emulation (Blanning, 1975; O'Hagan, 2006).”
Regression Density Estimation With Variational Methods and Stochastic Approximation (2012, methodology): “For an overview of the statistical analysis of computer models and model emulation, see O’Hagan (2006).”
Efficient Emulators for Multivariate Deterministic Functions (2008, background): “This makes it easier to generate predictive diagnostics for emulators, as in Rougier et al. (2007), or to embed an outerproduct emulator within a hierarchical statistical model, e.”
Remarks on multi-output Gaussian process regression (2018, background): “, small- or largescale regression [4, 5, 6], dimensionality reduction [7], Bayesian optimization [8], uncertainty quantification [9] and time-series analysis [10].”
Review of surrogate modeling in water resources (2012, methodology): “‘‘Model emulation’’ is another term referring to response surface surrogate modeling [O’Hagan, 2006].”
Kriging is well-suited to parallelize optimization (2010, positive): “This issue is sometimes addressed using a full bayesian treatment, as can be found in [43], or more recently in [15, 34, 39].”
The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei (2013, methodology): “Gaussian process emulation ( Currin et al. , 1991; Haylock and O’Hagan, 1996; O’Hagan, 2006) is used to estimate model simulations at untried points throughout the space of the uncertainty of the model parameters when the computer model under investigation is too computationally expensive to be run ”
Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models (2009, methodology): “The lack of variable selection is also part of the reason why we do not recommend the use of standard Gaussian process type models as in [11, 51] when there is a”