probabilistic-programming

As it stands, a significant portion of the SVI tutorial code is written in markdown code blocks, rather than standalone Jupyter cells. When formatted this way, the tutorial's notebook cannot be excecuted and experimented with by the reader. I suggest that the markdown code blocks should be refactored into Jupyter code ce

tfd.Categorical.quantile not working

Ankit Shah and I are trying to use Gen to support a project and would love the addition of a dirichlet distribution

I'm finally following up on @fehiepsi 's suggestion that I make this feature request following our conversation in this issue in the Arviz repo. But looking over the docs again, it looks like this may have already been implemented with a Tensorflow distribution that may make this FR unnecessary. Please feel free to close if that's the case!

Is your feature request related to a problem? Please describe.
While sales forecasting, it is necessary that the model is given the input about the promotions, special events that are taken care of in the prophet model as the holiday effect. Does orbit support this feature?

The current example on MDN from Edward tutorials needs small modifications to run on edward2. Documentation covering these modifications will be appreciated.

It is hard to keep track of how subsequent changes affect the sampling performance of the library. We should add ˋpytest-benchmark` to the test suite and use related github action to run benchmarks automatically.

There are a variety of interesting optimisations that can be performed on kernels of the form

k(x, z) = w_1 * k_1(x, z) + w_2 * k_2(x, z) + ... + w_L k_L(x, z)

A naive recursive implementation in terms of the current Sum and Scaled kernels hides opportunities for parallelism in the computation of each term, and the summation over terms.

Notable examples of kernels with th

See wikipedia table for example patterns: https://en.wikipedia.org/wiki/List_of_integrals_of_Gaussian_functions