Dev:
Algorithms for Learning Graphical Models
The Julia package manager can be used to install GraphicalModelLearning as follows,
] add GraphicalModelLearning
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.
If you find GraphicalModelLearning useful in your work, we kindly request that you cite the following publications:
@misc{1902.00600,
author = {Marc Vuffray and Sidhant Misra and Andrey Y. Lokhov},
title = {Efficient Learning of Discrete Graphical Models},
year = {2019},
eprint = {arXiv:1902.00600},
url = {https://arxiv.org/abs/1902.00600}
}
@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}
}
This code is provided under a BSD license as part of the Optimization, Inference and Learning for Advanced Networks project, C18014.