# How can I create a Gaussian process?

I'm writing a short summary of GPR for my coworkers. I want to give them an intuition into what a Gaussian process is without mentioning distributions over functions. Can you suggest how I can explain to them what a Gaussian process is in an easy to digest way?

You could try this way: hopefully your coworkers are familiar with linear regression, so start from there. You want to fit a curve through some training points, but instead than assuming a certain expression for the function (for example, a linear combination of monomial terms $\sum_{i=0}^p\beta_ix^i$, or a linear combination of trigonometric terms $\sum_{i=0}^p(\alpha_i\cos{\frac {2\pi}{L}ix}+\beta_i\cos{\frac{2\pi}{L}ix})$, or a nonlinear combination of monomials $\frac{\sum_{i=0}^m\alpha_ix^i}{1+\sum_{i=1}^p\beta_ix^i}$, etc.), you only want to say that the curve is this smooth, or that smooth, etc, and you show them realizations from GPs with different kernels (for example, see this answer).