The Libra Toolkit for Probabilistic Models
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Abstract (arXiv)
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, and sum-product networks. Compared to other toolkits, Libra places a greater emphasis on learning the structure of tractable models in which exact inference is efficient. It also includes a variety of algorithms for learning graphical models in which inference is potentially intractable, and for performing exact and approximate inference. Libra is released under a 2-clause BSD license to encourage broad use in academia and industry.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.
- 2021Probabilistic Generating Circuitscited by 24
- 2024Scaling Tractable Probabilistic Circuits: A Systems Perspectivecited by 23
- 2024Scaling Tractable Probabilistic Circuits: A Systems Perspective2 Jun 2024
- 2021Probabilistic Generating Circuits19 Feb 2021