Juice is an open-source Julia package providing tools for logic and probabilistic reasoning and learning based on logic circuits (LCs) and probabilistic circuits (PCs). It provides a range of efficient algorithms for probabilistic inference queries, such as computing marginal probabilities (MAR), as well as many more advanced queries. Certain structural circuit properties are needed to achieve this tractability, which Juice helps validate. Additionally, it supports several parameter and structure learning algorithms proposed in the recent literature. By leveraging parallelism (on both CPU and GPU), Juice provides a fast implementation of circuit-based algorithms, which makes it suitable for tackling large-scale datasets and models.

@inproceedings{DangAAAI21,
    title   = {Juice: A Julia Package for Logic and Probabilistic Circuits},
    author = {Dang, Meihua and Khosravi, Pasha and Liang, Yitao and Vergari, Antonio and Van den Broeck, Guy},
    booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track)},
    month   = 2,
    year    = {2021},
    url     = "http://starai.cs.ucla.edu/papers/DangAAAI21.pdf",
    code = "https://github.com/Juice-jl",
    keywords  = {conference}
}