A Gaussian process is a statistical method used to model and predict data points in a continuous space. It treats a collection of random variables, any finite number of which have a joint Gaussian distribution. This allows for flexible modeling of complex functions, making it useful in various fields like machine learning and geostatistics. In a Gaussian process, the mean and covariance functions define the properties of the data. The mean function indicates the expected value, while the covariance function describes how points in the space relate to each other. This framework enables uncertainty quantification in predictions, providing valuable insights in decision-making processes.