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        "author": "Quentin Leveque",
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        "description": "# Bullseye!\n\n\"Bullseye!\" is a new algorithm for computing the Gaussian Variational Approximation of a target distribution. Its strong point lies in the fact that it can easily be parallelized and distributed.\n\n## Getting Started\n\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.\n\n### Installing\n\nBullseye! is now available as a [PyPI package](https://pypi.python.org/pypi/bullseye_method/):\n\n```\npip install bullseye_method\n```\n\nor clone the repository :\n\n```\ngit clone https://github.com/Whenti/bullseye\n```\n\nor [download and extract the zip](https://github.com/Whenti/bullseye/archive/master.zip) into your project folder.\n\n## Running the tests\n\nTo see if everything is working properly, you can already run the algorithm on a multilogit model with artificially generated data.\n\n```py\nfrom Bullseye.Tests import simple_test\nsimple_test()\n```\n\n## Example\n\n```py\nimport Bullseye\nfrom Bullseye import generate_multilogit\n\ntheta_0, x_array, y_array = generate_multilogit(d = 10, n = 10**3, k = 5)\n\nbull = Bullseye.Graph()\nbull.feed_with(x_array,y_array)\nbull.set_predefined_model(\"multilogit\")\nbull.set_predefined_prior(\"normal_iid\")\nbull.init_with(mu_0 = 0, cov_0 = 1)\nbull.set_options(local_std_trick = True, s=5)\nbull.build()\n\nbull.run()\n```\n\n## Authors\n\n* **Quentin L\u00e9v\u00eaque** [Whenti](https://github.com/Whenti)\n\nSee also the list of [contributors](https://github.com/Whenti/bullseye/contributors) who participated in this project. Hopefully, there will be more.\n\n## License\n\nThis project licensed under the GPL3 License - see the [LICENSE.txt](LICENSE.txt) file for details.\n",
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        "summary": "Implemented tensorflow version of the Bullseye method for prior approximation.",
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