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Where tractable exact inference\nis used. Currently four different inference\nmethods are supported with more to come.\n\nGraphical Models Supported\n--------------------------\n\n- Bayesian Belief Networks with discrete variables\n- Gaussian Bayesian Networks with continous variables having gaussian distributions\n\n\nInference Engines\n-----------------\n\n- Message Passing and the Junction Tree Algorithm\n- The Sum Product Algorithm\n- MCMC Sampling for approximate inference\n- Exact Propagation in Gaussian Bayesian Networks\n\n\nOther Features\n--------------\n\n- Automated conversion to Junction Trees\n- Inference of Graph Structure from Mass Functions\n- Automatic conversion to Factor Graphs\n- Seemless storage of samples for future use\n- Exact inference on cyclic graphs\n- Export of graphs to GraphViz (dot language) format\n- Discrete and Continuous Variables (with some limitations)\n- Minimal dependancies on non-standard library modules.\n\nPlease see the short tutorial in the docs/tutorial directory\nfor a short introduction on how to build a Bayesian Belief Network.\nThere are also many examples in the examples directory.\n\n\nInstallation\n------------\n\n$ python setup.py install\n$ pip install -r requirements.txt\n\nBuilding The Tutorial\n\n$ pip install sphinx\n$ cd docs/tutorial\n$ make clean\n$ make html\n\nUnit Tests:\n\nTo run the tests in a development environment:\n\n$ PYTHONPATH=. py.test bayesian/test\n\n\nResources\n=========\n\nhttp://www.fil.ion.ucl.ac.uk/spm/course/slides10-vancouver/08_Bayes.pdf\nhttp://www.ee.columbia.edu/~vittorio/Lecture12.pdf\nhttp://www.csse.monash.edu.au/bai/book/BAI_Chapter2.pdf\nhttp://www.comm.utoronto.ca/frank/papers/KFL01.pdf\nhttp://www.snn.ru.nl/~bertk/ (Many real-world examples listed)\nhttp://www.cs.ubc.ca/~murphyk/Bayes/Charniak_91.pdf\nhttp://www.sciencedirect.com/science/article/pii/S0888613X96000692\nhttp://arxiv.org/pdf/1301.7394v1.pdf\n\nJunction Tree Algorithm:\nhttp://www.inf.ed.ac.uk/teaching/courses/pmr/docs/jta_ex.pdf\nhttp://ttic.uchicago.edu/~altun/Teaching/CS359/junc_tree.pdf\nhttp://eniac.cs.qc.cuny.edu/andrew/gcml/lecture10.pdf\nhttp://leo.ugr.es/pgm2012/proceedings/eproceedings/evers_a_framework.pdf\n\nGuassian Bayesian Networks:\nhttp://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Lectures/lec17x4.pdf\nhttp://webdocs.cs.ualberta.ca/~greiner/C-651/SLIDES/MB08_GaussianNetworks.pdf\nhttp://people.cs.aau.dk/~uk/papers/castillo-kjaerulff-03.pdf\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FeBay%2Fbayesian-belief-networks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FeBay%2Fbayesian-belief-networks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FeBay%2Fbayesian-belief-networks/lists"}