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. The package includes functionality to evaluate quadratic forms efficiently.


Reference manual

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1.2-21 by Robin K. S. Hankin, 2 months 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, rmarkdown

Imported by BayesianTools, MM, jordan, lorentz, multivator, onion, splmm.

Depended on by BACCO, approximator, calibrator, cmvnorm.

Suggested by elliptic, stokes.

See at CRAN