(GP-TProcess)=

Student-t Process

:::{post} August 2017 :tags: t-process, gaussian process, nonparametric :category: intermediate :author: Bill Engels :::

PyMC also includes T-process priors. They are a generalization of a Gaussian process prior to the multivariate Student's T distribution. The usage is identical to that of gp.Latent, except they require a degrees of freedom parameter when they are specified in the model. For more information, see chapter 9 of Rasmussen+Williams, and Shah et al..

Note that T processes aren't additive in the same way as GPs, so addition of TP objects are not supported.

Samples from a TP prior

The following code draws samples from a T process prior with 3 degrees of freedom and a Gaussian process, both with the same covariance matrix.

Poisson data generated by a T process

For the Poisson rate, we take the square of the function represented by the T process prior.

Authors

• Authored by Bill Engels
• Updated by Chris Fonnesbeck to use PyMC v5

References

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