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        "author": "Ed Schofield",
        "author_email": "ed@pythoncharmers.com",
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            "Development Status :: 4 - Beta",
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            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS",
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            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "# maxentropy: Maximum entropy and minimum divergence models in Python\n\n## Purpose\n\nThis package helps you to construct a probability distribution\n(Bayesian prior) from prior information that you encode as\ngeneralized moment constraints.\n\nYou can use it to either:\n\n1. find the flattest distribution that meets your constraints, using the\n   maximum entropy principle (discrete distributions only)\n\n2. or find the \"closest\" model to a given prior model (in a KL divergence\n   sense) that also satisfies your additional constraints.\n\n## Background\n\nThe maximum entropy principle has been shown [Cox 1982, Jaynes 2003] to be the unique consistent approach to\nconstructing a discrete probability distribution from prior information that is available as \"testable information\".\n\nIf the constraints have the form of linear moment constraints, then\nthe principle gives rise to a unique probability distribution of\n**exponential form**. Most well-known probability distributions are\nspecial cases of maximum entropy distributions. This includes\nuniform, geometric, exponential, Pareto, normal, von Mises, Cauchy,\nand others: see\n[here](https://en.wikipedia.org/wiki/Maximum_entropy_probability_distribution).\n\n## Examples: constructing a prior subject to known constraints\n\nSee the [notebooks folder](https://github.com/PythonCharmers/maxentropy/tree/master/notebooks).\n\n### Quickstart guide\nThis is a good place to start: [Loaded die example (scikit-learn estimator API)](https://github.com/PythonCharmers/maxentropy/blob/master/notebooks/Loaded%20die%20example%20-%20skmaxent.ipynb)\n\n## History\nThis package previously lived in SciPy \n(http://scipy.org) as ``scipy.maxentropy`` from versions v0.5 to v0.10.\nIt was under-maintained and removed from SciPy v0.11. It has since been\nresurrected and refactored to use the scikit-learn Estimator inteface.\n\n## Copyright\n(c) Ed Schofield, 2003-2019",
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        "keywords": "maximum-entropy minimum-divergence kullback-leibler-divergence KL-divergence bayesian-inference bayes scikit-learn sklearn prior prior-distribution",
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