The Libra Toolkit for Probabilistic Models
April 01, 2015 ยท Declared Dead ยท ๐ Journal of machine learning research
"No code URL or promise found in abstract"
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Authors
Daniel Lowd, Amirmohammad Rooshenas
arXiv ID
1504.00110
Category
cs.LG: Machine Learning
Cross-listed
cs.AI
Citations
31
Venue
Journal of machine learning research
Last Checked
4 months ago
Abstract
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.
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