Bayesian network learning with cutting planes

This webpage contains supplementary materials for:

The slides of a talk on this work given at the Gatsby Institute on 23 March 2011 are also available.

If anything does not work drop me a line.


Family scores

This is the data described in Table 1 of the paper and is supplied as a single gzipped tar file. The final 4 datasets are not included since they are not mine to distribute. Note that .tgz file does not create a new directory when unpacked.

These family scores are all you need if you want to try out an alternative approach to BN learning. Each 'family' is a BN variable together with a candidate set of parents. There is a score associated with each family. The score of any given DAG is just the sum of relevant family scores. 'Exact' BN learning from such data therefore consists of nothing more than choosing a parent set for each variable such that: (1) the resulting graph is acylic and (2) the score is maximal.

The format of the data is as follows:

This format originated with the work done in Learning Bayesian Network Structure using LP Relaxations. Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila. AISTATS 2010


Detailed results

The output produced by SCIP is available for the runs reported in Table 2 and Table 3. At the end of each file the BN itself is printed as a list of 'families'. Each family has its family score given. The last line is the time taken to do the SCIP run as measured by UNIX time (not SCIP).


Source code

GOBNILP, an improved version of the software used for this paper, is now available.


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