Posted in 2017
Student-t Process
- 12 August 2017
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..
Dependent density regression
- 12 May 2017
In another example, we showed how to use Dirichlet processes to perform Bayesian nonparametric density estimation. This example expands on the previous one, illustrating dependent density regression.
Updating Priors
- 12 January 2017
In this notebook, we will show how, in principle, it is possible to update the priors as new data becomes available.