Bayesian Optimization: Theory and Practice Using Python

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023

P. LiuBayesian Optimizationhttps://doi.org/10.1007/978-1-4842-9063-7_2

2. Gaussian Processes

Peng Liu¹  

(1)

Singapore, Singapore

 

In the previous chapter, we covered the derivation of the posterior distribution for parameter θ as well as the predictive posterior distribution of a new observation y^′ under a normal/Gaussian prior distribution. Knowing the posterior predictive distribution is helpful in supervised learning tasks such as regression and classification. In particular, the posterior predictive distribution quantifies the possible realizations and uncertainties of both existing and future observations (if we were to sample again). In this chapter, ...