**R – Statistical Odds & Ends**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

* Gaussian processes* are a widely employed statistical tool because of their flexibility and computational tractability. (For instance, one recent area where Gaussian processes are used is in machine learning for hyperparameter optimization.)

A stochastic process is a Gaussian process if (and only if) any finite subcollection of random variables has a multivariate Gaussian distribution. Here, is the index set for the Gaussian process; most often we have (to index time) or (to index space).

The stochastic nature of Gaussian processes also allows it to be thought of as a distribution over functions. One draw from a Gaussian process over corresponds to choosing a function according to some probability distribution over these functions.

Gaussian processes are defined by their mean and covariance functions. The covariance (or kernel) function is what characterizes the shapes of the functions which are drawn from the Gaussian process. * In this post, we will demonstrate how the choice of covariance function affects the shape of functions it produces.* For simplicity, we will assume .

(Click on this link to see all code for this post in one script. For more technical details on the covariance functions, see this previous post.)

**Overall set-up**

Let’s say we have a zero-centered Gaussian process denoted by , and that is a function drawn from this Gaussian process. For a vector , the function values must have a multivariate Gaussian distribution with mean and covariance matrix with entries . We make use of this property to draw this function: we select a fine grid of x-coordinates, use `mvrnorm()`

from the `MASS`

package to draw the function values at these points, then connect them with straight lines.

Assume that we have already written an R function `kernel_fn`

for the kernel. The first function below generates a covariance matrix from this kernel, while the second takes `N`

draws from this kernel (using the first function as a subroutine):

library(MASS) # generate covariance matrix for points in `x` using given kernel function cov_matrix <- function(x, kernel_fn, ...) { outer(x, x, function(a, b) kernel_fn(a, b, ...)) } # given x coordinates, take N draws from kernel function at those points draw_samples <- function(x, N, seed = 1, kernel_fn, ...) { Y <- matrix(NA, nrow = length(x), ncol = N) set.seed(seed) for (n in 1:N) { K <- cov_matrix(x, kernel_fn, ...) Y[, n] <- mvrnorm(1, mu = rep(0, times = length(x)), Sigma = K) } Y }

The `...`

argument for the `draw_samples()`

function allows us to pass arguments into the kernel function `kernel_fn`

.

We will use the following parameters for the rest of the post:

x <- seq(0, 2, length.out = 201) # x-coordinates N <- 3 # no. of draws col_list <- c("red", "blue", "black") # for line colors

**Squared exponential (SE) kernel**

The squared exponential (SE) kernel, also known as the * radial basis function (RBF) kernel* or the

*has the form*

**Gaussian kernel**where and

se_kernel <- function(x, y, sigma = 1, length = 1) { sigma^2 * exp(- (x - y)^2 / (2 * length^2)) } Y <- draw_samples(x, N, kernel_fn = se_kernel, length = 0.2) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = "SE kernel, length = 0.2") for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) }

The following code shows how changing the “length-scale” parameter `l`

affects the functions drawn. The smaller `l`

is, the more wiggly the functions drawn.

par(mfrow = c(1, 3)) for (l in c(0.2, 0.7, 1.5)) { Y <- draw_samples(x, N, kernel_fn = se_kernel, length = l) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = paste("SE kernel, length =", l)) for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) } }

**Rational quadratic (RQ) kernel**

The rational quadratic (RQ) kernel has the form

where `l`

affects the functions drawn:

rq_kernel <- function(x, y, alpha = 1, sigma = 1, length = 1) { sigma^2 * (1 + (x - y)^2 / (2 * alpha * length^2))^(-alpha) } par(mfrow = c(1, 3)) for (a in c(0.01, 0.5, 50)) { Y <- draw_samples(x, N, kernel_fn = rq_kernel, alpha = a) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = paste("RQ kernel, alpha =", a)) for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) } }

**Matérn covariance functions**

The Matérn covariance function has the form

where

matern_kernel <- function(x, y, nu = 1.5, sigma = 1, l = 1) { if (!(nu %in% c(0.5, 1.5, 2.5))) { stop("p must be equal to 0.5, 1.5 or 2.5") } p <- nu - 0.5 d <- abs(x - y) if (p == 0) { sigma^2 * exp(- d / l) } else if (p == 1) { sigma^2 * (1 + sqrt(3)*d/l) * exp(- sqrt(3)*d/l) } else { sigma^2 * (1 + sqrt(5)*d/l + 5*d^2 / (3*l^2)) * exp(-sqrt(5)*d/l) } } par(mfrow = c(1, 3)) for (nu in c(0.5, 1.5, 2.5)) { Y <- draw_samples(x, N, kernel_fn = matern_kernel, nu = nu) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = paste("Matern kernel, nu =", nu * 2, "/ 2")) for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) } }

The paths from the Matérn-1/2 kernel are often deemed too rough to be used in practice.

**Periodic kernel**

The periodic kernel has the form

where

period_kernel <- function(x, y, p = 1, sigma = 1, length = 1) { sigma^2 * exp(-2 * sin(pi * abs(x - y) / p)^2 / length^2) } par(mfrow = c(1, 3)) for (p in c(0.5, 1, 2)) { Y <- draw_samples(x, N, kernel_fn = period_kernel, p = p) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = paste("Periodic kernel, p =", p)) for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) } }

**Linear/polynomial kernel**

The polynomial kernel has the form

where

poly_kernel <- function(x, y, sigma = 1, d = 1) { (sigma^2 + x * y)^d } # linear kernel w different sigma par(mfrow = c(1, 3)) for (s in c(0.5, 1, 5)) { Y <- draw_samples(x, N, kernel_fn = poly_kernel, sigma = s) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = paste("Linear kernel, sigma =", s)) for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) } } # poly kernel of different dimensions par(mfrow = c(1, 3)) for (d in c(1, 2, 3)) { Y <- draw_samples(x, N, kernel_fn = poly_kernel, d = d) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = paste("Polynomial kernel, d =", d)) for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) } }

**Brownian motion**

Brownian motion, the most studied object in stochastic processes, is a one-dimensional Gaussian process with mean zero and covariance function

bm_kernel <- function(x, y) { pmin(x, y) } Y <- draw_samples(x, N, kernel_fn = bm_kernel) plot(range(x), range(Y), xlab = "x", ylab = "y", type = "n", main = "Brownian motion kernel") for (n in 1:N) { lines(x, Y[, n], col = col_list[n], lwd = 1.5) }

Note that I had to use the `pmin()`

function instead of `min()`

in the Brownian motion covariance function. This is because the `outer(X, Y, FUN, ...)`

function we called in `cov_matrix()`

“…[extends

`X`

and`Y`

] by`rep`

to length the products of the lengths of`X`

and`Y`

before`FUN`

is called.” (from`outer()`

documentation)

`pmin()`

would perform the operation we want, while `min()`

would simply return the minimum value present in the two arguments, not what we want.

**leave a comment**for the author, please follow the link and comment on their blog:

**R – Statistical Odds & Ends**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.