I’m defining my negative binomial like this currently: dispersion = pm.HalfNormal(‘dispersion’, 1) sigma = mean_rate * (1 + mean_rate / dispersion) counts = pm.NegativeBinomial(“counts”, mu=rates, alpha=sigma, observed=event_count) So the dispersion is moderating the over-dispersion of the negat…

Prior for negative binomial dispersion moderator

cluhmann September 9, 2022, 9:29pm 3

Seems like the easiest solution would be to use a deterministic?

disp_base = pm.HalfNormal("disp_base", 0, 1)
dispersion = pm.Deterministic("dispersion", pm.math.sqr(1 / disp_base))

Topic		Replies	Views
Non-Centered Reparameterisation for the Negative Binomial Distribution? Questions	5	794	January 23, 2021
Need help drawing a RV from a custom prior function Questions	6	2142	September 17, 2019
Re-coding a partial pooling from pymc to pymc3 Questions	1	467	November 2, 2018
Mirrored and bounded HalfNormal Distribution Questions	3	864	April 5, 2019
Hierarchical logistic regression giving non-sensible results? Do I formulate it correctly? Questions	15	897	August 4, 2020