Hi!

I have a very basic inference problem, that has a single prior belief and a set of observed values that i want to infer a posterior from.

The thing here is that the values are extremely small. I have the following model:

```
obs = np.ones(100)*10**-6
prior_mean = 1e-8
prior_sd = 1e-6
with pm.Model() as model:
prior = pm.Normal('prior', mu=prior_mean, sd = prior_sd)
pm.Normal('likelihood', mu=prior, observed=obs)
trace = pm.sample()
pm.tracpelot(trace)
```

However, although the observed values are 100 times bigger than the prior, the posterior looks identical to the prior. This would not have happened if the prior was 0 and the observed values were 100 for example.

How do i deal with this small values issue? I have tried centering the prior without any result

Thank you for helping out!