Control-data separation and logical condition propagation for efficient inference on probabilistic programs

We present a novel sampling framework for probabilistic programs. The framework combines two recent ideas-control-data separation and 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. (c) 2023 Elsevier Inc. All rights reserved.