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Predictive weights are Sigma^-1*y in standard GP. This calculation is much faster since we don't need to solve the full system of equations.
CGGP_internal_calcpw(CGGP, y, theta, return_lS = FALSE)
CGGP object
Measured values for CGGP$design
Correlation parameters
Should lS be returned?
Vector with predictive weights
# NOT RUN { cggp <- CGGPcreate(d=3, batchsize=100) y <- apply(cggp$design, 1, function(x){x[1]+x[2]^2+rnorm(1,0,.01)}) CGGP_internal_calcpw(CGGP=cggp, y=y, theta=cggp$thetaMAP) # }
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