PyBBN is Python library for Bayesian Belief Networks (BBNs) exact inference using the junction tree algorithm or Probability Propagation in Trees of Clusters. The implementation is taken directly from C. Huang and A. Darwiche, "Inference in Belief Networks: A Procedural Guide," in International Journal of Approximate Reasoning, vol. 15, pp. 225--263, 1999.
Below is an example code to create a Bayesian Belief Network, transform it into a join tree, and then set observation evidence. The last line prints the marginal probabilities for each node.
# create the nodes
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
d = BbnNode(Variable(3, 'd', ['on', 'off']), [0.9, 0.1, 0.5, 0.5])
e = BbnNode(Variable(4, 'e', ['on', 'off']), [0.3, 0.7, 0.6, 0.4])
f = BbnNode(Variable(5, 'f', ['on', 'off']), [0.01, 0.99, 0.01, 0.99, 0.01, 0.99, 0.99, 0.01])
g = BbnNode(Variable(6, 'g', ['on', 'off']), [0.8, 0.2, 0.1, 0.9])
h = BbnNode(Variable(7, 'h', ['on', 'off']), [0.05, 0.95, 0.95, 0.05, 0.95, 0.05, 0.95, 0.05])
# create the network structure
bbn = Bbn()\
.add_node(a)\
.add_node(b)\
.add_node(c)\
.add_node(d)\
.add_node(e)\
.add_node(f)\
.add_node(g)\
.add_node(h)\
.add_edge(Edge(a, b, EdgeType.DIRECTED))\
.add_edge(Edge(a, c, EdgeType.DIRECTED))\
.add_edge(Edge(b, d, EdgeType.DIRECTED))\
.add_edge(Edge(c, e, EdgeType.DIRECTED))\
.add_edge(Edge(d, f, EdgeType.DIRECTED))\
.add_edge(Edge(e, f, EdgeType.DIRECTED))\
.add_edge(Edge(c, g, EdgeType.DIRECTED))\
.add_edge(Edge(e, h, EdgeType.DIRECTED))\
.add_edge(Edge(g, h, EdgeType.DIRECTED))
# convert the BBN to a join tree
join_tree = InferenceController.apply(bbn)
# insert an observation evidence
ev = EvidenceBuilder()\
.with_node(join_tree.get_bbn_node_by_name('a'))\
.with_evidence('on', 1.0)\
.build()
join_tree.set_observation(ev)
# print the marginal probabilities
for node in join_tree.get_bbn_nodes():
potential = join_tree.get_bbn_potential(node)
print(node)
print(potential)To build, you will need Python 3.5. Type in the following.
make
To build the documents, go into the docs sub-directory and type in the following.
make html
Here is a list of other Python libraries for inference in Bayesian Belief Networks.
| Library | Algorithm | Algorithm Type | License |
|---|---|---|---|
| BayesPy | variational message passing | approximate | MIT |
| pomegranate | loopy belief | approximate | MIT |
| pgmpy | multiple | approximate/exact | MIT |
| libpgm | likelihood sampling | approximate | Proprietary |
| bayesnetinference | variable elimination | exact | None |
I found other packages in PyPI too.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent