Name | veroku JSON |

Version | 0.0.28 JSON |

download | |

home_page | https://github.com/ejlouw/veroku |

Summary | An open source library for building and performing inference with probabilistic graphical models. |

upload_time | 2020-09-17 06:13:27 |

maintainer | |

docs_url | None |

author | EJ Louw |

requires_python | >=3.6 |

license | BSD 3-Clause License |

keywords | |

VCS | |

bugtrack_url | |

requirements | No requirements were recorded. |

Travis-CI | No Travis. |

coveralls test coverage | No coveralls. |

<div align="center"> <img src="logo.png"> </div> ### Disclaimer This project is still in the very early stages of development. Use this library at your own risk. The github repo is also still private, but will be made public soon. ### Overview <div style="text-align: justify"> Veroku is an open source library for building and performing inference with probabilistic graphical models (PGMs). PGMs provide a framework for performing efficient probabilistic inference with very high dimensional distributions. A typical example of a well-known type of PGM is the Kalman filter that can be used to obtain probabilistic estimates of a hidden state of a process or system, given noisy measurements. PGMs can in principle be used for any problem that involves uncertainty and is therefore applicable to many problems.</div> Veroku currently supports the following distributions: * Multivariate Gaussian * Multivariate Gaussian mixture * Multivariate categorical <div style="text-align: justify"> These distributions can be used as factors to represent a factorised distribution. These factors can be used, together with the `cluster_graph` module to automatically create valid cluster graphs. Inference can be performed in these graphs using message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported. </div> <br/><br/> ### Future Features To be added soon: * Example notebooks * Non-linear Gaussian distribution * Plate models (for efficiently specifying PGMs as modular/hierarchical templates) On the roadmap: * Dirichlet distribution * Wishart distribution * Normal-Wishart distribution

{ "_id": null, "home_page": "https://github.com/ejlouw/veroku", "name": "veroku", "maintainer": "", "docs_url": null, "requires_python": ">=3.6", "maintainer_email": "", "keywords": "", "author": "EJ Louw", "author_email": "ejlouw00@gmail.com", "download_url": "https://files.pythonhosted.org/packages/14/0b/864257db9e35e4099e4a41ea114e3e62e5182fc10102451dffcd2c072cca/veroku-0.0.28.tar.gz", "platform": "", "description": "<div align=\"center\">\n <img src=\"logo.png\">\n</div>\n\n### Disclaimer\nThis project is still in the very early stages of development. Use this library at your own risk. The github repo is also still private,\nbut will be made public soon.\n\n### Overview\n<div style=\"text-align: justify\">\nVeroku is an open source library for building and performing inference with probabilistic graphical models (PGMs). PGMs\nprovide a framework for performing efficient probabilistic inference with very high dimensional distributions. A typical\nexample of a well-known type of PGM is the Kalman filter that can be used to obtain probabilistic estimates of a hidden\nstate of a process or system, given noisy measurements. PGMs can in principle be used for any problem that involves\nuncertainty and is therefore applicable to many problems.</div> \n\nVeroku currently supports the following distributions:\n* Multivariate Gaussian\n* Multivariate Gaussian mixture\n* Multivariate categorical\n\n<div style=\"text-align: justify\">\nThese distributions can be used as factors to represent a factorised distribution. These factors can be used, together\nwith the `cluster_graph` module to automatically create valid cluster graphs. Inference can be performed in these graphs\nusing message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported.\n</div>\n\n<br/><br/>\n\n### Future Features\nTo be added soon:\n* Example notebooks\n* Non-linear Gaussian distribution\n* Plate models (for efficiently specifying PGMs as modular/hierarchical templates)\n\nOn the roadmap:\n* Dirichlet distribution\n* Wishart distribution\n* Normal-Wishart distribution\n\n\n\n", "bugtrack_url": null, "license": "BSD 3-Clause License", "summary": "An open source library for building and performing inference with probabilistic graphical models.", "version": "0.0.28", "split_keywords": [], "urls": [ { "comment_text": "", "digests": { "md5": "1d5d3b4a02afac8e6ace0e79c4b9924b", "sha256": "c0a7175972b7379603e4fdb395933a6570bfcfc9ac4703bd61e154986dff2d8d" }, "downloads": -1, "filename": "veroku-0.0.28-py3-none-any.whl", "has_sig": false, "md5_digest": "1d5d3b4a02afac8e6ace0e79c4b9924b", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 43771, "upload_time": "2020-09-17T06:13:26", "upload_time_iso_8601": "2020-09-17T06:13:26.262779Z", "url": "https://files.pythonhosted.org/packages/00/88/61926b66d84713887518cf8a4213512507d10e75b26c3d7d91d43fe0ec29/veroku-0.0.28-py3-none-any.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "24618eb59ffb2b34e331cd53faa13c47", "sha256": "ce554645736e07d341d6e9ce1b72732171a9766fddf035f9196a1d59adedfebe" }, "downloads": -1, "filename": "veroku-0.0.28.tar.gz", "has_sig": false, "md5_digest": "24618eb59ffb2b34e331cd53faa13c47", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 139044, "upload_time": "2020-09-17T06:13:27", "upload_time_iso_8601": "2020-09-17T06:13:27.482704Z", "url": "https://files.pythonhosted.org/packages/14/0b/864257db9e35e4099e4a41ea114e3e62e5182fc10102451dffcd2c072cca/veroku-0.0.28.tar.gz", "yanked": false, "yanked_reason": null } ], "upload_time": "2020-09-17 06:13:27", "github": true, "gitlab": false, "bitbucket": false, "github_user": null, "github_project": "ejlouw", "error": "Could not fetch GitHub repository", "lcname": "veroku" }

Elapsed time: 0.15120s