We built our Gaussian process regression model using the Python package Scikit-learn . A Gaussian process is a nonparametric model which uses a Bayesian approach to sample a posterior distribution of functions . There are two priors set for a Gaussian process, a mean function and a kernel (or covariance) function. As with most Gaussian process models, we set our prior mean function to zero. When initially training and validating the model, we considered a variety of the kernel functions included in Scikit-learn. To choose a kernel and optimize any required parameters, we used a three-fold cross validation method considering the root mean squared error (RMSE), mean error, and correlation coefficient of the training and validating sets (Section 2.5). The best performing kernel for our purposes was a Matérn kernel -a generalized function between the squared and absolute exponential kernels . This kernel requires a preset parameter ν which was optimized to 2.5 for our model. The general form of Matérn kernel is complex including a Bessel function, and with ν = 2.5, our final kernel is the function: