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A Process over all Stationary Covariance Kernels

I define a process over all stationary covariance kernels. I show how one might be able to perform inference that scales as O(nm) in a GP regression model using this process as a prior over the covariance kernel, with n datapoints and m < n. I also show how the stationarity assumption can be relaxed.