Stable Graphical Model Learning (StabLe) is an algorithm for learning the structure and parameters of stable graphical (SG) models from data.

Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. SG models are multi-variate stable distributions that represent Bayesian networks whose edges encode linear dependencies amongst random variables. A preprint version of the manuscript describing stable graphical models is available at http://arxiv.org/abs/1404.4351.

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Categories

Machine Learning

License

GNU General Public License version 3.0 (GPLv3)

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Additional Project Details

Intended Audience

Science/Research

Programming Language

C++

Related Categories

C++ Machine Learning Software

Registered

2014-04-14