将Stan编译为生成概率语言,并扩展到深度概率规划

Guillaume Baudart, Javier Burroni, Martin Hirzel, Louis Mandel, Avraham Shinnar
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引用次数: 6

摘要

Stan是一种在统计社区中很流行的概率编程语言,具有用于表示概率模型的高级语法。Stan在本质上不同于Church、Anglican或Pyro等生成概率编程语言。本文提出了一种将任意Stan模型编译成生成语言的综合编译方案,并证明了其正确性。我们使用我们的编译方案为针对Pyro和NumPyro的Stanc3编译器构建两个新的后端。实验结果表明,在26个基准测试中,NumPyro后端比Stan的几何平均速度提高了2.3倍。在Pyro的基础上,我们扩展了Stan,支持显式变分推理指南和深度概率模型。通过这种方式,熟悉Stan的用户无需学习一门全新的语言就可以访问新功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming
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.
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