greta.gp 0.2.0

Bridges in Perth, film, Nick Tierney

We are excited to announce that greta.gp version 0.2.0 is now on CRAN!

greta.gp provides a syntax to create and combine Gaussian process kernels in greta. You can then them to define either full rank or sparse Gaussian processes.

Here’s a brief example:

# 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)

# fit the model by Hamiltonian Monte Carlo
m <- model(f_plot)
draws <- mcmc(m, n_samples = 250)

# plot 200 posterior samples
plot(
  y ~ x,
  pch = 16,
  col = grey(0.4),
  xlim = c(0, 10),
  ylim = c(-2.5, 2.5)
)
for (i in 1:200) {
  lines(
    draws[[1]][i, ] ~ x_plot,
    lwd = 2,
    col = rgb(0.7, 0.1, 0.4, 0.1)
  )
}