Hello, I recently purchased Allen Downey’s Think Bayes - I’m working through Chapter 20 - Counting Cells (the chapter can be opened in Google Colab here). There is a question I’m pondering that affects some of my models. It seems that we do not want all random variables to be adaptive (i.e., have changeable parameters). For example, in the snippet below, we want `yeast_conc`

to be adaptive because that is what we are inferring. But we do not want `shaker1_vol`

to be adaptive because it is measured outside of the model; it is a stochastic, but we don’t want `mu`

and `sd`

to be changed by sampling.

```
with pm.Model() as model:
yeast_conc = pm.Normal("yeast conc",
mu=2 * billion, sd=0.4 * billion)
shaker1_vol = pm.Normal("shaker1 vol",
mu=9.0, sd=0.05)
```

My question is two-part, one related to this example, and one general:

- For anyone who has worked this example, do you agree that
`shaker1_vol`

should be a frozen/ non-adaptive distribution rather than a random variable, as presently coded? - How would we go about coding a non-adaptive stochastic variable into a model in pymc3? I considered adding a hierarchical layer where
`mu`

and`sd`

are really narrow truncated priors to constrain`shaker1_vol`

. But maybe there is a better way if this is a common problem.

Thanks!

Greg