The Libra Toolkit for Probabilistic Models
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.
Cite
Text
Lowd and Rooshenas. "The Libra Toolkit for Probabilistic Models." Machine Learning Open Source Software, 2015.Markdown
[Lowd and Rooshenas. "The Libra Toolkit for Probabilistic Models." Machine Learning Open Source Software, 2015.](https://mlanthology.org/mloss/2015/lowd2015jmlr-libra/)BibTeX
@article{lowd2015jmlr-libra,
title = {{The Libra Toolkit for Probabilistic Models}},
author = {Lowd, Daniel and Rooshenas, Amirmohammad},
journal = {Machine Learning Open Source Software},
year = {2015},
pages = {2459-2463},
volume = {16},
url = {https://mlanthology.org/mloss/2015/lowd2015jmlr-libra/}
}