Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs
Machine Learning
2023-10-02 v3 Programming Languages
Abstract
We present a novel sampling framework for probabilistic programs. The framework combines two recent ideas -- \emph{control-data separation} and \emph{logical condition propagation} -- in a nontrivial manner so that the two ideas boost the benefits of each other. We implemented our algorithm on top of Anglican. The experimental results demonstrate our algorithm's efficiency, especially for programs with while loops and rare observations.
Cite
@article{arxiv.2101.01502,
title = {Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs},
author = {Ichiro Hasuo and Yuichiro Oyabu and Clovis Eberhart and Kohei Suenaga and Kenta Cho and Shin-ya Katsumata},
journal= {arXiv preprint arXiv:2101.01502},
year = {2023}
}