I’ve always enjoyed the Gaussian Process part of the PyMC package, and a question on the mailing list yesterday reminded me of a project I worked on with it that never came to fruition: how to implement constraints on the derivatives of the GP.

The best answer I could come up with is to use “potential” nodes, and do it approximately. That is to say, instead of constraining the derivative, I satisfy myself to constrain a secant that approximates the derivative. And instead of constraining it at every point in an interval, I satisfy myself to constrain it at a discrete subset of points.

Here is an ipython notebook example: [ipynb] [py]

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Tagged as gaussian processes