Probabilistic programming is becoming increasingly popular thanks to its ability to specify problems with a certain degree of uncertainty. In this work, we focus on term rewriting, a well-known computational formalism. In particular, we consider systems that combine traditional rewriting rules with probabilities. Then, we define a novel "distribution semantics" for such systems that can be used to model the probability of reducing a term to some value. We also show how to compute a set of "explanations" for a given reduction, which can be used to compute its probability in a more efficient way. Finally, we illustrate our approach with several examples and outline a couple of extensions that may prove useful to improve the expressive power of probabilistic rewrite systems.
View on arXiv@article{vidal2025_2410.15081,
title={ A Distribution Semantics for Probabilistic Term Rewriting },
author={ Germán Vidal },
journal={arXiv preprint arXiv:2410.15081},
year={ 2025 }
}