learning GP hyperparameters
(1.6 hours to learn)
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.
This concept has the prerequisites:
Core resources (read/watch one of the following)
Supplemental resources (the following are optional, but you may find them useful)
→ 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
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