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This distribution was derived in a research note by Hardie & Fader. It's a simple alternative to the Beta-Geometric mixture distribution, and can be easily extended to support static and time-varying covariates.
I started working on this for a CLV model in pymc-marketing, but internal discussions were in favor of adding this to pymc-extras instead because this distribution has broad application.
The research note only provides a PMF and survival function. You can walk through my whiteboarding of the log-likelihood and log-CDF in the below image:
The text was updated successfully, but these errors were encountered:
Migrated from pymc-devs/pymc#7733.
This distribution was derived in a research note by Hardie & Fader. It's a simple alternative to the Beta-Geometric mixture distribution, and can be easily extended to support static and time-varying covariates.
I started working on this for a CLV model in
pymc-marketing
, but internal discussions were in favor of adding this topymc-extras
instead because this distribution has broad application.The research note only provides a PMF and survival function. You can walk through my whiteboarding of the log-likelihood and log-CDF in the below image:
The text was updated successfully, but these errors were encountered: