|Authors||Daniel Lowd <firstname.lastname@example.org> and Amirmohammad (Pedram) Rooshenas <email@example.com>|
|Maintainers||Daniel Lowd <firstname.lastname@example.org> and Amirmohammad (Pedram) Rooshenas <email@example.com>|
|Available||ocaml-version >= "4.00.0" & ocaml-version < "4.03.0"|
|Published||Jun 16, 2015|
|Statistics||Installed 9 times last month.|
Learning and inference with discrete probabilistic models
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks (BNs), Markov networks (MNs), dependency networks (DNs), sum-product networks (SPNs), and arithmetic circuits (ACs). Compared to other toolkits, Libra focuses more on structure learning, especially for tractable models in which exact inference is efficient. Each algorithm in Libra is implemented as a command-line program suitable for interactive use or scripting, with consistent options and file formats throughout the toolkit.
|No package is dependent|