The Libra Toolkit for Probabilistic Models
This toolkit provides tools for researchers and practitioners working with probabilistic models, but it is incremental as it builds upon existing methods without introducing new paradigms.
The authors introduced the Libra Toolkit, a collection of algorithms for learning and inference with discrete probabilistic models, emphasizing tractable models for efficient exact inference and including methods for intractable cases.
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.