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: In order to run the unit tests you need the pytest framwork. This can be installed in a virtuanlenv with: $ pip install pytest 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 GuassianVariableNode 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