In the last chapter, we learned about the Dirichlet process, an infinite-dimensional generalization of the Dirichlet distribution that can be used to set a prior on unknown continuous distributions. In this chapter, we will learn about the Gaussian process, an infinite-dimensional generalization of the Gaussian distribution that can be used to set a prior on unknown functions. Both the Dirichlet process and the Gaussian process are used in Bayesian statistics to build flexible models where the number of parameters is allowed to increase with the size of the data.
In this chapter, we will cover the following topics:

• Functions as probabilistic objects
• Kernels
• Gaussian processes with Gaussian likelihoods
• Gaussian processes with non-Gaussian likelihoods