http://areshenkblog.com/priors-for-variance-parameters-in-hierarchical-models/ ]]>

>Also, I disagree that the priors “bias the group standard deviation.” If the prior information is real, the priors aren’t biasing anything! That’s Bayesian inference for you.

It gives you a massively different set of values, and I suspect that this will tank predictive performance. But I can’t check that without a proper simulation study and it’s a Friday afternoon.

]]>We did use the zero-avoiding gamma(2) priors for point estimation, but here I was actually thinking of stronger priors, maybe even lognormal, that really do exclude zero for real. I’d discussed this with Jonah in the ofc but I guess that didn’t make it into my post.

Also, I disagree that the priors “bias the group standard deviation.” If the prior information is real, the priors aren’t biasing anything! That’s Bayesian inference for you.

]]>