Watch 1 Star 0

码云极速下载 / bayesian-belief-networks

Sign up for free
Explore and code with more than 2 million developers,Free private repositories !:)
Sign up
此仓库是为了提升国内下载速度的镜像仓库,每日同步一次。 原始仓库:
Nothing here. spread retract

Clone or download
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.


$ python 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

========= (Many real-world examples listed)

Junction Tree Algorithm:

Guassian Bayesian Networks:

Comments ( 0 )

You need to Sign in for post a comment

Help Search