## A conceptual guide

Gaussian processes (GPs) are a flexible class of nonparametric machine learning models commonly used for modeling spatial and time series data. A common application of GPs is regression. For example, given incomplete geographical weather data, such as temperature or humidity, how can we recover values at unobserved locations? If we have good reason to believe the data is normally distributed, then a using a GP model could be a judicious choice. In what follows, we introduce the mechanics behind the GP model and then illustrate its use in recovering missing data.

Formally speaking, a GP is a stochastic process, or a distribution over functions. The premise is that the function values are themselves random variables. When modeling a function as a Gaussian process, we are making the assumption that any finite number of sampled points form a multivariate normal distribution.