We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches to this problem rely on designing a single global variational guide on a variable-by-variable basis, while maintaining the stochastic control flow of the original program. SDVI instead breaks the program down into sub-programs with static support, before automatically building separate sub-guides for each. This decomposition significantly aids in the construction of suitable variational families, enabling, in turn, substantial improvements in inference performance.

翻译：我们提出支撑分解变分推断（SDVI），这是一种用于具有随机支撑的概率程序的新型变分推断方法。现有方法依赖于在保持原程序随机控制流的同时，基于逐个变量设计单一全局变分指导。SDVI则先将程序分解为具有静态支撑的子程序，再为每个子程序自动构建独立的子指导。这种分解显著促进了合适变分族的构建，进而大幅提升了推断性能。