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Theory Gaussian processes are non-parametric models that define a distribution over a function where the function itself is a random variable of some inputs $X$. They can be thought of as a distribution over infinite dimensions but computation can be done using finite resources. This property makes them useful for many spacial and temporal prediction tasks. A Gaussian Process prior is parameterized by a mean function and a covariance function. Given these parameters, a GP prior can be defined as...