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GraphicalModelLearning

Algorithms for Learning Graphical Models

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GraphicalModelLearning.jl

Dev: Build Status codecov

Algorithms for Learning Graphical Models

Installation

In julia run,

using Pkg
Pkg.clone("https://github.com/lanl-ansi/GraphicalModelLearning.jl.git")

Quick Start

Try the following commands in julia,

using GraphicalModelLearning

model = FactorGraph([0.0 0.1 0.2; 0.1 0.0 0.3; 0.2 0.3 0.0])
samples = sample(model, 100000)
learned = learn(samples)

err = abs.(convert(Array{Float64,2}, model) - learned)

Note that the first invocation of learn will be slow as the dependent libraries are compiled. Subsequent calls will be fast.

Reference

If you find GraphicalModelLearning useful in your work, we kindly request that you cite the following publications:

@article{Lokhove1700791,
    author = {Lokhov, Andrey Y. and Vuffray, Marc and Misra, Sidhant and Chertkov, Michael},
    title = {Optimal structure and parameter learning of Ising models},
    journal = {Science Advances}
    volume = {4}, number = {3}, year = {2018},
    publisher = {American Association for the Advancement of Science},
    doi = {10.1126/sciadv.1700791},
    URL = {http://advances.sciencemag.org/content/4/3/e1700791}
}
@incollection{NIPS2016_6375,
    author = {Vuffray, Marc and Misra, Sidhant and Lokhov, Andrey Y. and Chertkov, Michael},
    title = {Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models},
    booktitle = {Advances in Neural Information Processing Systems 29},
    year = {2016}, pages = {2595--2603},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/6375-interaction-screening-efficient-and-sample-optimal-learning-of-ising-models.pdf}
}
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## License

This code is provided under a BSD license as part of the Optimization, Inference and Learning for Advanced Networks project, C18014.

First Commit

11/11/2017

Last Touched

11 days ago

Commits

64 commits

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