Bayesian Emulation of Computer Programs

Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. A vignette is provided and the help pages include examples.


Reference manual

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1.2-20 by Robin K. S. Hankin, 3 days ago

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Browse source code at

Authors: Robin K. S. Hankin [aut, cre]

Documentation:   PDF Manual  

GPL license

Depends on mvtnorm

Suggests knitr

Imported by BayesianTools, MM, lorentz, multivator.

Depended on by BACCO, approximator, calibrator, cmvnorm, lmmlasso.

Suggested by elliptic.

See at CRAN