Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming
By Guillaume Baudart, Javier Burroni, Martin Hirzel, Louis Mandel, Avraham Shinnar. 2021
15 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistics and Probability
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Citations in the corpus
- Foundation Posteriors for Approximate Probabilistic Inference (2022, neutral): “To demonstrate the foundation posterior, we meta-amortize inference over a set of standard Stan [12] programs from PosteriorDB [43], a benchmark dataset for evaluating inference algorithms [4, 5, 69, 70, 20].”
- Automatic Guide Generation for Stan via NumPyro (2021, methodology): “We recently proposed new backends for the Stanc3 Compiler to Pyro and NumPyro1 and showed how to extend Stan with support for explicit variational guides [Baudart et al. 2021].”