# Appropriate Gamma prior for ARD regression
**URL:** https://discourse.pymc.io/t/appropriate-gamma-prior-for-ard-regression/13648
**Category:** v5
**Tags:** prior, modeling
**Created:** [January 18, 2024, 4:33pm UTC](https://discourse.pymc.io/t/appropriate-gamma-prior-for-ard-regression/13648 "2024-01-18T16:33:27Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@Gumibear\_Toronto](https://discourse.pymc.io/u/Gumibear_Toronto)
#### Post date: [January 18, 2024, 4:33pm UTC](https://discourse.pymc.io/t/appropriate-gamma-prior-for-ard-regression/13648/1 "2024-01-18T16:33:27Z")
Hello,
I am implementing ARD regression problems and I got a question. It seems like we usually imply very flat gamma priors for the ARD regression problem such as Gamma(0.01,0.01) as parameter’s variances. The prior shape can be considered as flat; however, isn’t it we are implying “most of variables are highly relevant to Y”? This is because a variable’s coefficient parameter distribution can only go to 0 when the mean of gamma is increasing (which means if the variables are relevant, variance should be small).
In this regard, I was just wondering if I can use Gamma(1,1) or Gamma(2,0.01) priors for the ARD regression models. Or how do you guys approaches for these issues on what appropriate priors are.
Thanks,
Jay
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### Author:  [@Simon](https://discourse.pymc.io/u/Simon)
#### Post date: [January 25, 2024, 2:28pm UTC](https://discourse.pymc.io/t/appropriate-gamma-prior-for-ard-regression/13648/2 "2024-01-25T14:28:27Z")
Hi. Have you tried prior calibration via prior predictive checks? Here’s a PyMC entry on prior predictives: [Prior and Posterior Predictive Checks — PyMC 5.10.3 documentation](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/posterior_predictive.html) . Hope it helps.