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Libra-Tk
Algorithms for learning and inference with discrete probabilistic models.

About Libra-Tk
Libra-Tk is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, and arithmetic circuits. It focuses on structure learning, especially for tractable models where exact inference is efficient.
Each algorithm is provided as a command-line program for interactive use or scripting, with consistent options and file formats across the toolkit.
Key features
- Learn tree-structured Bayesian networks with Chow-Liu algorithm
- Learn dependency networks and Markov networks from dependency networks
- Learn compact arithmetic circuits for Bayesian and Markov networks
- Learn sum-product network structure with ID-SPN algorithm
- Run Gibbs sampling, belief propagation, max-product, and mean field inference
- Score models with likelihood and condition models on evidence
- Focus on structure learning for tractable models with efficient exact inference
- Consistent options and file formats across command-line programs
- Supports Bayesian networks, Markov networks, dependency networks, sum-product networks, and arithmetic circuits
- Handles discrete probabilistic models only
- Discriminative structure learning of arithmetic circuits is listed as coming in a future update
Libra-Tk at a glance
- Website
- libra.cs.uoregon.edu
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Libra-Tk FAQ
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