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

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bayesian
docs/tutorial
.gitignore
LICENSE.txt
MANIFEST.in
README.txt
requirements.txt
setup.py

README.txt

Pythonic Bayesian Belief Network Framework

Allows creation of BBNs with pure Python
functions. Currently three different inference
methods are supported with more to come:

- Message Passing and the Junction Tree Algorithm
- The Sum Product Algorithm
- MCMC Sampling for approximate inference


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

Please see the short tutorial in the docs/tutorial directory
for a short introduction on how to build a BBN.
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

Todo:

1) Change requirement for PMFs to use .value
2) Rename VariableNode to DiscreteVariableNode
3) Add GaussianVariableNode for continuous variables

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
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