Bayesian networks provide an attractive representation of structured probabilistic information. There is thus much interest in ‘learning’ BNs from data. In this paper the problem of learning a Bayesian network using integer programming is presented. The SCIP (Solving Constraint Integer Programming) framework is used to do this. Although cutting planes are a key ingredient in our approach, primal heuristics and efficient propagation are also important.
|Number of pages||12|
|Journal||Quality Technology and Quantitative Management|
|Publication status||Published - Mar 2014|
- Bayesian networks
- Integer programming
- Machine learning