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
AuthorsDaniel Lowd <lowd@cs.uoregon.edu> and Amirmohammad (Pedram) Rooshenas <pedram@cs.uoregon.edu>
LicenseBSD-2-clause
Published
Homepagehttp://libra.cs.uoregon.edu
Issue Trackerhttps://bitbucket.org/libra-tk/libra-tk/issues
MaintainersDaniel Lowd <lowd@cs.uoregon.edu> and Amirmohammad (Pedram) Rooshenas <pedram@cs.uoregon.edu>
Dependencies
Source [http] http://libra.cs.uoregon.edu/libra-tk-1.1.2d.tar.gz
md5=a53e35d844ba391d5053416696c48168
StatisticsInstalled 3 times last month.
Edithttps://github.com/ocaml/opam-repository/tree/master/packages/libra-tk/libra-tk.1.1.2/opam
No package is dependent