# AePPL and custom distributions

Linked to AePPL and transformed variables. But we should also investigate the idea of having a `RandomVariableFromGraph` operator, that works similarly to `OpFromGraph`.

We can already condition on and sample from the following:

```import aesara.tensor as at
import aesara
import aeppl

srng = at.random.RandomStream()

def pert(srng, a, b, c):
r"""Construct a random variable that is PERT-distributed."""
alpha = 1 + 4 * (b - a) / (c - a)
beta = 1 + 4 * (c - b) / (c - a)
X_rv = srng.beta(alpha, beta)
z = a + (b - a) * X_rv
return z

A_rv = srng.uniform(10, 20, name="A")
B_rv = srng.uniform(20, 65, name="B")
C_rv = srng.uniform(65, 100, name="C")
Y_rv = pert(srng, A_rv, B_rv, C_rv)

logprob, (y_vv, a_vv, b_vv, c_vv) = aeppl.joint_logprob(Y_rv, A_rv, B_rv, C_rv)

# Compile a function that samples from the prior predictive distribution
sample_fn = aesara.function([], [Y_rv, A_rv, B_rv, C_rv])
sample = sample_fn()
print(sample)

# Compile the joint log-density function
logprob_fn = aesara.function([y_vv, a_vv, b_vv, c_vv], logprob)
print(logprob_fn(*sample))
```

This would allow us to reduce the number of elementary distributions in Aesara/AePPL, and allow users to quickly add the distributions they need. By giving them a type we can use them in RV algebra relations.