The Libra Toolkit for Probabilistic Models

April 01, 2015 ยท Declared Dead ยท ๐Ÿ› Journal of machine learning research

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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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