Hello, I was trying to reproduce a model from a research paper, long story short, in their model, they have a 2D matrix denoted as **H**, where they said each entry in **H** has a prior distribution **beta ~ (u,sigma)**, and the **u** and **sigma** can be determined by the domain knowledge. The important point is each entry follows its own beta distribution, so **H_{ij} ~ Beta(u_{ij},sigma_{ij})**, I believe this can be easily implemented like below:

```
# assuming H is 100*100
mu = np.empty((100,100))
sigma = np.empty((100,100))
H = pm.Beta(mu=mu,sigma=sigma,shapes=(100,100))
```

But following that, they said each column of **H** must sum up to one, because of that, each column of matrix **H** has a prior distribution of **Dirichlet(alpha)**, where alpha is a vector of length 100. Now it seems that I have to write something like below:

```
H = pm.Dirichlet(alpha=np.empty((100,100)),shape=(100,100))
```

My question is, is it valid in Pymc to define a stochastic variable that is generated from two different distributions at the same time as I haven’t seen such things in the tutorial? I apologize if my question is too naive as I am just learning the bayesian modeling and am happy to further clarify my question.

Many thanks in advance,

Frank