# What is a good strategy to model individual noise parameters hierarchically (with partial pooling)?

Hi,

I am trying to fit a noise/scale parameter to individuals, allowing for some partial pooling.

I thought about doing something analogous to the normal non-centered parameterization for location parameters. As values must be positive, I try to work in log-scale and exponentiate the result of the operation, but I am not sure if this makes sense (and if it does, whether I am doing it correctly):

noise_pop_log = pm.Normal('noise_pop_log', mu=0, sd=5)   # The unit scale of the data is rather large, hence the sd=5 in log-scale