Gaussian Processes

Learn about Gaussian process and its importance in Bayes' theorem.

What is a Gaussian process?

A Gaussian process (GP) is a probabilistic model used in machine learning for regression and classification tasks. It is a powerful tool for modeling complex, nonlinear functions, and it can be used to make predictions even when the underlying function is not fully understood.

A GP is a collection of random variables, any finite number of which have a joint Gaussian distribution. The main idea behind GPs is that a set of data points is generated by a process governed by a probability distribution.

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