# Syntactic Requirements for Well-defined Hybrid Probabilistic Logic Programs

arXiv: https://arxiv.org/abs/2109.08283 · HTML: https://mlsanity.com/paper/2109.08283/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains](https://mlsanity.com/paper/2006.08480/) (2020)
- [On the Semantics and Complexity of Probabilistic Logic Programs](https://mlsanity.com/paper/1701.09000/) (2017)
- [Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming](https://mlsanity.com/paper/1807.00614/) (2018)

## Built on by (most cited first, 2 of 2)

- [Declarative Probabilistic Logic Programming in Discrete-Continuous Domains](https://mlsanity.com/paper/2302.10674/) (2023)
- [Explanations as Programs in Probabilistic Logic Programming](https://mlsanity.com/paper/2210.03021/) (2022)

## Abstract (arXiv)

Hybrid probabilistic logic programs can represent several scenarios thanks to the expressivity of Logic Programming extended with facts representing discrete and continuous distributions. The semantics for this type of programs is crucial since it ensures that a probability can be assigned to every query. Here, following one recent semantics proposal, we illustrate a concrete syntax, and we analyse the syntactic requirements needed to preserve the well-definedness.