# 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.