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Generating Bayesian Belief Networks

Let's generate some Bayesian Belief Networks (BBNs). The algorithms are taken from Random Generation of Bayesian Networks :cite:`2002:ide`. There are two types of BBNs you may generate.

  • singly-connected
  • multi-connected

A singly-connected BBN is one, where ignoring the direction of the edges, there is at most one path between any two nodes. A multi-connected BBN is one that is not singly-connected.

.. graphviz::
   :align: center
   :alt: Singly-connected network structure.
   :caption: Singly-connected network structure.

   digraph {
        node [fixedsize=true, width=0.3, shape=circle, fontname="Helvetica-Outline", color=crimson, style=filled]

        A -> C
        B -> C
        C -> D
        C -> E
        E -> F
   }


.. graphviz::
   :align: center
   :alt: Multi-connected network structure.
   :caption: Multi-connected network structure. There are two paths between C and F: (C, D, F) and (C, E, F).

   digraph {
        node [fixedsize=true, width=0.3, shape=circle, fontname="Helvetica-Outline", color=crimson, style=filled]

        A -> C
        B -> C
        C -> D
        C -> E
        D -> F
        E -> F
   }

Singly-Connected

The key method to use here is generate_singly_bbn.

.. literalinclude:: code/generate-singly.py
   :language: python
   :linenos:
   :emphasize-lines: 10

Multi-Connected

The key method to use here is generate_multi_bbn.

.. literalinclude:: code/generate-multi.py
   :language: python
   :linenos:
   :emphasize-lines: 10

Direct Generation

In the case where you do NOT need a reference to the BBN objects, use the API's convenience method to generate and serialize the BBN directly to file.

.. literalinclude:: code/api-generation.py
   :language: python
   :linenos:
   :emphasize-lines: 9, 12

Here's the output for singly-bbn.csv.

.. literalinclude:: code/singly-bbn.csv
   :language: text
   :linenos:

Here's the output for multi-bbn.csv.

.. literalinclude:: code/multi-bbn.csv
   :language: text
   :linenos: