https://github.com/eBay/bayesian-belief-networks

Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions.
https://github.com/eBay/bayesian-belief-networks

Last synced: 7 days ago
JSON representation

Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions.

• Host: GitHub
• URL: https://github.com/eBay/bayesian-belief-networks
• Owner: eBay
• License: other
• Archived: true
• Created: 2013-08-01T17:27:40.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2021-04-20T17:10:24.000Z (over 3 years ago)
• Last Synced: 2024-08-02T14:10:49.511Z (3 months ago)
• Language: Python
• Homepage:
• Size: 1.7 MB
• Stars: 1,125
• Watchers: 90
• Forks: 258
• Open Issues: 14
• Metadata Files:
  • Readme: README.txt
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
Pythonic Bayesian Belief Network Framework

------------------------------------------

Allows creation of Bayesian Belief Networks

and other Graphical Models with pure Python

functions. Where tractable exact inference

is used. Currently four different inference

methods are supported with more to come.

Graphical Models Supported

--------------------------

- Bayesian Belief Networks with discrete variables

- Gaussian Bayesian Networks with continous variables having gaussian distributions

Inference Engines

-----------------

- Message Passing and the Junction Tree Algorithm

- The Sum Product Algorithm

- MCMC Sampling for approximate inference

- Exact Propagation in Gaussian Bayesian Networks

Other Features

--------------

- Automated conversion to Junction Trees

- Inference of Graph Structure from Mass Functions

- Automatic conversion to Factor Graphs

- Seemless storage of samples for future use

- Exact inference on cyclic graphs

- Export of graphs to GraphViz (dot language) format

- Discrete and Continuous Variables (with some limitations)

- Minimal dependancies on non-standard library modules.

Please see the short tutorial in the docs/tutorial directory

for a short introduction on how to build a Bayesian Belief Network.

There are also many examples in the examples directory.

Installation

------------

$ python setup.py install

$ pip install -r requirements.txt

Building The Tutorial

$ pip install sphinx

$ cd docs/tutorial

$ make clean

$ make html

Unit Tests:

To run the tests in a development environment:

$ PYTHONPATH=. py.test bayesian/test

Resources

=========

http://www.fil.ion.ucl.ac.uk/spm/course/slides10-vancouver/08_Bayes.pdf

http://www.ee.columbia.edu/~vittorio/Lecture12.pdf

http://www.csse.monash.edu.au/bai/book/BAI_Chapter2.pdf

http://www.comm.utoronto.ca/frank/papers/KFL01.pdf

http://www.snn.ru.nl/~bertk/ (Many real-world examples listed)

http://www.cs.ubc.ca/~murphyk/Bayes/Charniak_91.pdf

http://www.sciencedirect.com/science/article/pii/S0888613X96000692

http://arxiv.org/pdf/1301.7394v1.pdf

Junction Tree Algorithm:

http://www.inf.ed.ac.uk/teaching/courses/pmr/docs/jta_ex.pdf

http://ttic.uchicago.edu/~altun/Teaching/CS359/junc_tree.pdf

http://eniac.cs.qc.cuny.edu/andrew/gcml/lecture10.pdf

http://leo.ugr.es/pgm2012/proceedings/eproceedings/evers_a_framework.pdf

Guassian Bayesian Networks:

http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Lectures/lec17x4.pdf

http://webdocs.cs.ualberta.ca/~greiner/C-651/SLIDES/MB08_GaussianNetworks.pdf

http://people.cs.aau.dk/~uk/papers/castillo-kjaerulff-03.pdf