一句话：提出信息流类型系统捕获概率模型的条件独立性，并用类型推断与程序变换自动消除离散参数。
概率编程语言希望分离建模与推断，但用户常需改写模型以提升推断效率或满足限制。条件独立性（CI）是模型的关键定性性质，可用于改善推断。本文提出面向概率编程的信息流类型系统来捕获 CI：若程序良类型，其实现的分布必满足相应 CI 关系；通过类型推断可静态推导模型中的 CI 性质。作为应用，针对离散与连续混合参数模型，作者给出保语义的源到源变换，用 CI 类型系统自动消去离散参数，形成混合推断：连续参数用基于梯度的推断，离散参数用变量消元。系统在 Stan 的组合式变体 SlicStan 中实现。
A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to re-write their models to improve efficiency of inference or meet restrictions imposed by the PPL. Conditional independence (CI) relationships among parameters are a crucial aspect of probabilistic models that capture a qualitative summary of the specified model and can facilitate more efficient inference. We present an information flow type system for probabilistic programming that captures conditional independence (CI) relationships and show that, for a well-typed program in our system, the distribution it implements is guaranteed to have certain CI-relationships. Further, by using type inference, we can statically deduce which CI-properties are present in a specified model. As a practical application, we consider the problem of how to perform inference on models with mixed discrete and continuous parameters. Inference on such models is challenging in many existing PPLs, but can be improved through a workaround, where the discrete parameters are used implicitly , at the expense of manual model re-writing. We present a source-to-source semantics-preserving transformation, which uses our CI-type system to automate this workaround by eliminating the discrete parameters from a probabilistic program. The resulting program can be seen as a hybrid inference algorithm on the original program, where continuous parameters can be drawn using efficient gradient-based inference methods, while the discrete parameters are inferred using variable elimination. We implement our CI-type system and its example application in SlicStan: a compositional variant of Stan. 1
@article{paperbot1205,
title = {Conditional Independence by Typing},
author = {Maria I. Gorinova and Andrew D. Gordon and Charles Sutton and Matthijs Vákár},
journal = {ACM Transactions on Programming Languages and Systems},
volume = {44},
number = {1},
year = {2021},
doi = {10.1145/3490421}
}