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arXiv · 2101.01502

Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs

Ichiro Hasuo·Yuichiro Oyabu·Clovis Eberhart·Kohei Suenaga·Kenta Cho·Shin-ya Katsumata
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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.

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Ichiro Hasuo, Yuichiro Oyabu, Clovis Eberhart, Kohei Suenaga, Kenta Cho, Shin-ya Katsumata. 2021-01-05. Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs. https://arxiv.org/abs/2101.01502

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