Hey, I have a question on how I could implement **observed** marginal probabilities.

Normally you define:

```
x_variable = pyro.sample(f"x_variable", dist.Categorical(p_))
```

However, from my understanding you define the prior in this way. For example:

alpha = pyro.sample(‘alpha’, dist.Normal(0.0, 1.0))

this defines the parameter alpha with a normal(0,1) prior. I am not interested in estimating p_ but rather keep it fixed when modelling. I know there must be a simple way to implement this. Yet, I can’t find it…

Could someone help me?