library(gp.regression)
data("mcycle", package = "MASS")
x <- seq(from=0, to=max(mcycle$times), length.out=100)
# homoscedastic gaussian process
# ------------------------------------------------------------------------------
gp <- new.gp(0.0, kernel.squared.exponential(5, 100))
gp <- posterior(gp, mcycle$times, mcycle$accel, 20.0)
plot(gp, x)
# heteroscedastic gaussian process
# ------------------------------------------------------------------------------
x11(type="cairo")
gp <- new.gp.heteroscedastic(
new.gp( 0.0, kernel.squared.exponential(4, 100)),
new.gp(10.0, kernel.squared.exponential(4, 10),
likelihood=new.likelihood("gamma", 1),
link=new.link("logistic")),
transform = sqrt,
transform.inv = function(x) x^2)
gp <- posterior(gp, mcycle$times, mcycle$accel, 0.00001,
step = 0.1,
epsilon = 0.000001,
verbose=T)
plot(gp, x)
plot(gp$gp.h, x)
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