Description
I would like to contribute a guide for defining custom PyMC3 distributions. I was working with count data and realized that the Poisson distribution didn't offer the flexibility in dispersion I needed since the mean and variance are fixed to each other. I decided that the Generalized Poisson was a better likelihood for my model, so I wrote a custom distribution for it. I have put together a notebook outlining this, and I'll link my PR to this issue.
I posted about this on discourse a while back, so it would be great to post updates for the community in this thread:
https://discourse.pymc.io/t/generalized-poisson-distribution/6535
Since I have now worked with the Generalized Poisson in PyMC3, I would also like to contribute this distribution to the main project: pymc-devs/pymc#4775
File:
Reviewers:
The sections below may still be pending. If so, the issue is still available, it simply doesn't
have specific guidance yet. Please refer to this overview of updates
Known changes needed
Changes listed in this section should all be done at some point in order to get this
notebook to a "Best Practices" state. However, these are probably not enough!
Make sure to thoroughly review the notebook and search for other updates.
General updates
ArviZ related
Changes for discussion
Changes listed in this section are up for discussion, these are ideas on how to improve
the notebook but may not have a clear implementation, or fix some know issue only partially.