# Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming

arXiv: https://arxiv.org/abs/1807.00614 · HTML: https://mlsanity.com/paper/1807.00614/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Syntactic Requirements for Well-defined Hybrid Probabilistic Logic Programs](https://mlsanity.com/paper/2109.08283/) (2021)

## Abstract (arXiv)

In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain encompassing additionally continuous random variables. Inference in the hybrid domain, however, usually necessitates to condone trade-offs on either the inference on discrete or continuous random variables. We introduce a novel approach based on weighted model integration and algebraic model counting that circumvents these trade-offs. We then show how it supports knowledge compilation and exact probabilistic inference. Moreover, we introduce the hybrid probabilistic logic programming language HAL-ProbLog, an extension of ProbLog, to which we apply our inference approach.