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.
|Author||Robin K. S. Hankin|
|Date of publication||2018-05-17 04:14:33 UTC|
|Maintainer||Robin K. S. Hankin <[email protected]>|
|Package repository||View on CRAN|
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