# learning GP hyperparameters

(1.6 hours to learn)

## Summary

In order to apply Gaussian processes in practice, it is necessary to fit the hyperparameters of the model, such as the lengthscale and variance of a squared-exp kernel. Marginal likelihood is one commonly used criterion for doing so.

## Context

This concept has the prerequisites:

- Gaussian process regression (GP regression is the simplest setting in which to discuss hyperparameter learning.)
- Bayesian model comparison (The optimization criterion is based on Bayesian model comparison.)

## Core resources (read/watch one of the following)

## -Free-

→ Gaussian Processes for Machine Learning

A graduate-level machine learning textbook focusing on Gaussian processes.

## Supplemental resources (the following are optional, but you may find them useful)

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 6.4.3, pages 311-312

## See also

-No Additional Notes-