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greta
greta.gp
extends greta to let you define Gaussian processes as part of your model. It provides a syntax to create and combine GP kernels, and use them to define either full rank or sparse Gaussian processes.
You can install the current release version of greta.gp
from CRAN:
Or install the development version of greta.dynamics
from r-universe:
You can also install the development version of greta.gp
via GitHub:
# simulate data
x <- runif(20, 0, 10)
y <- sin(x) + rnorm(20, 0, 0.5)
x_plot <- seq(-1, 11, length.out = 200)
library(greta)
library(greta.gp)
# hyperparameters
rbf_var <- lognormal(0, 1)
rbf_len <- lognormal(0, 1)
obs_sd <- lognormal(0, 1)
# kernel & GP
kernel <- rbf(rbf_len, rbf_var) + bias(1)
f <- gp(x, kernel)
# likelihood
distribution(y) <- normal(f, obs_sd)
# prediction
f_plot <- project(f, x_plot)
# plot 200 posterior samples
# plot 200 posterior samples
plot(
y ~ x,
pch = 16,
col = grey(0.4),
xlim = c(0, 10),
ylim = c(-2.5, 2.5),
las = 1,
fg = grey(0.7),
)
for (i in 1:200) {
lines(draws[[1]][i, ] ~ x_plot,
lwd = 2,
col = rgb(0.7, 0.1, 0.4, 0.1))
}
Please note that the greta.gp project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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