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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: 8 days ago
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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 distributionsInference Engines
------------------ Message Passing and the Junction Tree Algorithm
- The Sum Product Algorithm
- MCMC Sampling for approximate inference
- Exact Propagation in Gaussian Bayesian NetworksOther 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.txtBuilding The Tutorial
$ pip install sphinx
$ cd docs/tutorial
$ make clean
$ make htmlUnit 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.pdfJunction 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.pdfGuassian 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