Bayesian Emulation of Computer Programs
Calculates MLE coefficients of linear fit
correlation function for calculating A
tools:::Rd_package_title("emulator")
Estimates each known datapoint using the others as datapoints
Interpolates between known points using Bayesian estimation
Latin hypercube design matrix
Makes input files for condor runs of goldstein
Simple model for concept checking
Implementation of the ideas of Oakley and O'Hagan 2002
Use optimization techniques to find the optimal scales
Simple pad function
Prior linear fits
Evaluate a quadratic form efficiently
Regressor basis function
Variance estimator
Sample from a Gaussian process and fit an emulator to the points
Likelihood of roughness parameters
Estimator for sigma squared
Trace of a matrix
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.