= Gaussian process regression
{c}
Gaussian process regression estimates an unknown <function> by assigning its values a <Gaussian process> law, then conditioning on observed values, often with independent <Gaussian noise>. The <covariance function> expresses the proposed relation between nearby inputs; the <conditional multivariate normal distribution> gives the resulting <Gaussian process regression posterior>. Its conditional <mean> provides a prediction and its conditional <variance> quantifies uncertainty under the chosen model. The <index set> can represent spatial locations or other inputs as well as time.
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