GPR is a probabilistic model representing a collection of random variables that follow a joint Gaussian distribution. As a non-parametric Bayesian method, it can adapt to varying data complexity. GPR model assumes that the function f that relates the inputs to the outputs is drawn from a Gaussian process with mean function 𝜇 and covariance function k. The distribution of f at a set of test points x * is then given by: 𝑓(𝑥 * ) ∼ N(μ(𝑥 * ), k(𝑥 * , 𝑥 * ))