libra-tkversion
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.
Tags | clib:expat |
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Authors | Daniel Lowd <lowd@cs.uoregon.edu> and Amirmohammad (Pedram) Rooshenas <pedram@cs.uoregon.edu> |
License | BSD-2-clause |
Published | |
Homepage | http://libra.cs.uoregon.edu |
Issue Tracker | https://bitbucket.org/libra-tk/libra-tk/issues |
Maintainers | Daniel Lowd <lowd@cs.uoregon.edu> and Amirmohammad (Pedram) Rooshenas <pedram@cs.uoregon.edu> |
Dependencies |
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Source [http] | http://libra.cs.uoregon.edu/libra-tk-1.1.2d.tar.gz sha256=88948d298611f4139919e9ff974506912b07a2bef4944e5aeabd25007d72e0d9 md5=a53e35d844ba391d5053416696c48168 |
Edit | https://github.com/ocaml/opam-repository/tree/master/packages/libra-tk/libra-tk.1.1.2/opam |
No package is dependent