{ "info": { "author": "Pedro Melgueira", "author_email": "pedromelgueira@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: End Users/Desktop", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering", "Topic :: Software Development :: Libraries" ], "description": "ProbPy is a Python library that aims to simplify calculations with discrete multi variable probabilistic distributions by offering an abstraction over how data is stored and how the operations between distributions are performed.\n\n The library can be used in the implementation of many algorithms such as Bayes Theorem, Bayesian Inference algorithms like Variable Elimination, Gibbs Ask (MCMC), HMMs implementations, Information Theory, etc.\n\n Currently, there are implementation for Bayesian and Markov Networks with some inference algorithms implemented.\n\n For more information check the GitHub page at: https://github.com/petermlm/ProbPy.", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/petermlm/ProbPy", "keywords": "probability calculus random variable bayes bayesian network information theory markov", "license": "MIT License", "maintainer": null, "maintainer_email": null, "name": "ProbPy", "package_url": "https://pypi.org/project/ProbPy/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/ProbPy/", "project_urls": { "Download": "UNKNOWN", "Homepage": "https://github.com/petermlm/ProbPy" }, "release_url": "https://pypi.org/project/ProbPy/1.1/", "requires_dist": null, "requires_python": null, "summary": "Multi Variable Probability Calculus Library", "version": "1.1" }, "last_serial": 1251571, "releases": { "1.0": [ { "comment_text": "", "digests": { "md5": "a37bfabbb3cce41d4057180fd3e9ae88", "sha256": "9275ec8a26f036eed6eb00e3a33e7d2a1bd1a554ec015a29b82a704783db3318" }, "downloads": -1, "filename": "ProbPy-1.0.tar.gz", "has_sig": false, "md5_digest": "a37bfabbb3cce41d4057180fd3e9ae88", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 15867, "upload_time": "2014-10-08T08:49:18", "url": "https://files.pythonhosted.org/packages/37/b2/12949bd4393ae84d39578dea3d2d7a2d47ac78c3b0bc92843cf6f120142c/ProbPy-1.0.tar.gz" } ], "1.1": [ { "comment_text": "", "digests": { "md5": "762fb12f4f15186e07ec174d8f4dea22", "sha256": "659849558b87155e6e425820ab8b58440c440aa9612d786e6709ca5d1c59ac5d" }, "downloads": -1, "filename": "ProbPy-1.1.tar.gz", "has_sig": false, "md5_digest": "762fb12f4f15186e07ec174d8f4dea22", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 15855, "upload_time": "2014-10-08T08:50:59", "url": "https://files.pythonhosted.org/packages/1b/31/74c4e28b8cc4db719564f3b313d623df21e16345330de6f18a1d76e5243f/ProbPy-1.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "762fb12f4f15186e07ec174d8f4dea22", "sha256": "659849558b87155e6e425820ab8b58440c440aa9612d786e6709ca5d1c59ac5d" }, "downloads": -1, "filename": "ProbPy-1.1.tar.gz", "has_sig": false, "md5_digest": "762fb12f4f15186e07ec174d8f4dea22", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 15855, "upload_time": "2014-10-08T08:50:59", "url": "https://files.pythonhosted.org/packages/1b/31/74c4e28b8cc4db719564f3b313d623df21e16345330de6f18a1d76e5243f/ProbPy-1.1.tar.gz" } ] }