I’m wondering whether it’s possible to construct a directed acyclic graph (DAG), i.e., a Bayesian network and perform posterior inference with Pyro package? For example, a graph describing the following generative process:
x ~ N(\mu, \theta)
e ~ N(f(x), g(x))
y ~ N(h(x,e), m(x,e))
i.e., y < x > e > y, where f(.), g(.), h(.), m(.) are some link functions.
My goal to perform posterior inference p(y|x).
I couldn’t find any related materials from Pyro online tutorial or in this forum. Any guidance would be appreciated!