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        "description": "# Particle Gibbs for Bayesian classification and structure learning in decomposable graphical models\nA graphical predictive classifier and a particle Gibbs sampler for Bayesian structure learning in discrete log-linear and Gaussian decomposable graphical models.\n\n### Installation\n\nIf graphviz is not installed, you can install it from brew / aptitude / pacman for example\n```\n$ brew install graphviz\n```\nThen run\n```\n$ pip install trilearn\n```\n\n### Running the tests\n\n```\n$ make test\n```\n\n## Scripts\n### Continuous data\nTo approximate the underlying decomposable graph posterior given the dataset sample_data/data_ar1-5.csv run\n```\n$ pgibbs_ggm_sample -N 50 -M 1000 -f sample_data/data_ar1-5.csv\n```\nthis will produce a file containing the Markov chain generated by the particle Gibbs algorithm. \nIn order to analyze the chain run\n```\n$ analyze_graph_tajectories\n```\nthis will produce a bunch of files in the current directory to be analyzed.\n\n### Discrete data\nThe data set examples/data/czech_autoworkers.csv contains six binary variables.\nTo generate a particle Gibbs trajectory of decomposable graphs type\n```\n$ pgibbs_loglinear_sample -N 50 -M 300 -f sample_data/czech_autoworkers.csv\n```\nand\n```\n$ analyze_graph_tajectories\n```\nthis will produce a number of files in the current directory.\n\n### Estimate the number of decomposable graphs\nTo estimate the number of decomposable graphs with up to 15 nodes run for example\n```\n$ count_chordal_graphs -p 15 -N 20000\n```\n## Built With\n\n* [NetworkX](https://networkx.github.io/documentation/stable/index.html)\n* [NumPy](https://docs.scipy.org/doc/)\n* [Scipy](https://docs.scipy.org/doc/)\n* [Pandas](http://pandas.pydata.org/pandas-docs/stable/)\n* [Seaborn](https://seaborn.pydata.org/api.html)\n## Authors\n\n* **Felix Rios**\n\n## References\n* [Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios, Sequential sampling of junction trees for decomposable graphs,\n ArXiv 2018](https://arxiv.org/abs/1806.00584)\n* [Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios, Bayesian inference in decomposable graphical models using sequential Monte Carlo methods, ArXiv 2018](https://arxiv.org/abs/1805.12571)\n* [Tetyana Pavlenko, Felix L. Rios, Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs, ArXiv 2018](https://arxiv.org/abs/1707.06792)\n\n## License\n\nThis project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details\n\n## Acknowledgments\n\n* **Jim Holmstrom**\n\n\n",
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