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        "description": "# Kiefer-Wolfowitz Nonparametric Empirical Bayes\nCompute the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixtures. \n\nIn contrast to the previous approaches, the optimization problem is reformulated into a convex problem by \n[Koenker and Mizera (2014)](http://www.stat.ualberta.ca/~mizera/Preprints/brown.pdf)'s method and efficiently solved by \ninterior-point method.\n\n## Making Predictions With No Features - A Basic Usage\nGiven a training set T = {y_i}, the algorithm provides a way to construct a predictor of future y-values such that the sum \nof squared errors between observations and predictors is minimized.  \n\n## Getting Started\n### Prerequisites \nYou will need:\n* python (>= 3.6)\n* pip (>= 19.0.3)\n* MOSEK (>=8.1.30) \n\nImportant about MOSEK:\n* MOSEK is a commercial optimization software. Please visit [MOSEK](https://www.mosek.com/) for license information.\n* PIP: \n```\npip install -f https://download.mosek.com/stable/wheel/index.html Mosek --user\n``` \nFor different ways of installation, please visit their [installation page](https://docs.mosek.com/8.1/pythonapi/install-interface.html).\n* MOSEK needs to be installed in the GLOBAL environment. \n\n\n\n### Installing\n```\npip install kwnpeb\n```\n\n## Examples\n* [simple](https://github.com/sit836/KW_NPEB/tree/master/examples/simple) - The basic usage\n* [bayesball](https://github.com/sit836/KW_NPEB/tree/master/examples/bayesball) - In-season prediction of batting averages with the 2005 Major\nLeague baseball\n\n## Contributors\n* [Sile Tao](https://ca.linkedin.com/in/sile-tao-95523941)\n* [Li Zhang](https://ca.linkedin.com/in/li-zhang-0350833b)\n* [Guanqi Huang](https://ca.linkedin.com/in/guanqi-huang)\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n\n",
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