Inference Plans for Hybrid Particle Filtering
Author(s)
Cheng, Ellie
Atkinson, Eric
Baudart, Guillaume
Mandel, Louis
Carbin, Michael
Date Issued
January 7, 2025
Journal
Proceedings of the ACM on Programming Languages
Publisher
Association for Computing Machinery
Citation
Cheng, Ellie, Atkinson, Eric, Baudart, Guillaume, Mandel, Louis and Carbin, Michael. 2025. "Inference Plans for Hybrid Particle Filtering." Proceedings of the ACM on Programming Languages, 9 (Proceedings of the ACM on Programming Languages).
Version
Final published version
Abstract
Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and the heuristics are not necessarily aligned with the developer's performance evaluation metrics. In this work, we present inference plans, a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering. We further present Siren, a new PPL that enables developers to use annotations to specify inference plans the inference system must implement. To assist developers with statically reasoning about whether an inference plan can be implemented, we present an abstract-interpretation-based static analysis for Siren for determining inference plan satisfiability. We prove the analysis is sound with respect to Siren's semantics. Our evaluation applies inference plans to three different hybrid particle filtering algorithms on a suite of benchmarks. It shows that the control provided by inference plans enables speed ups of 1.76x on average and up to 206x to reach a target accuracy, compared to the inference plans implemented by default heuristics; the results also show that inference plans improve accuracy by 1.83x on average and up to 595x with less or equal runtime, compared to the default inference plans. We further show that our static analysis is precise in practice, identifying all satisfiable inference plans in 27 out of the 33 benchmark-algorithm evaluation settings.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3704846
