  Stan is a probabilistic programming language that is popular in the
statistics community, with a high-level syntax for expressing probabilistic
models. Stan differs by nature from generative probabilistic programming
languages like Church, Anglican, or Pyro. This paper presents a comprehensive
compilation scheme to compile any Stan model to a generative language and
proves its correctness. We use our compilation scheme to build two new backends
for the Stanc3 compiler targeting Pyro and NumPyro. Experimental results show
that the NumPyro backend yields a 2.3x speedup compared to Stan in geometric
mean over 26 benchmarks. Building on Pyro we extend Stan with support for
explicit variational inference guides and deep probabilistic models. That way,
users familiar with Stan get access to new features without having to learn a
fundamentally new language.
