This package 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 working example is given in the help page for function `interpolant()', which should be the first point of reference.
|Author||Robin K. S. Hankin|
|Date of publication||2014-09-08 06:58:23|
|Maintainer||Robin K. S. Hankin <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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