M. Studeny, D. Haws:
Learning Bayesian network structure: towards the essential graph by integer linear programming tools.
International Journal of Approximate Reasoning
55 (2014), pp. 1043-1071.
The basic idea of the geometric approach to learning a Bayesian network (BN)
structure is to represent every BN structure by a certain
vector. If the vector representative is chosen properly,
it allows one to re-formulate the task of finding the global maximum of a
score over BN structures as an integer linear programming (ILP) problem.
Such a suitable zero-one vector representative is the characteristic imset,
introduced by Studeny, Hemmecke and Lindner in 2010, in the proceedings of the
5th PGM workshop. In this paper, extensions of characteristic imsets are considered
which additionally encode chain graphs without flags equivalent to acyclic
directed graphs. The main contribution is a polyhedral description of the respective domain
of the ILP problem, that is, by means of a set of linear inequalities. This theoretical result
opens the way to the application of ILP software packages. The advantage of our approach is that,
as a by-product of the ILP optimization procedure, one may get the essential graph,
which is a traditional graphical BN representative.
We also describe some computational experiments based on this idea.
- AMS classification 68T30 52B12 62H05
- learning Bayesian network structure
- integer linear programming
- characteristic imset
- essential graph
pdf version of the published paper (477kB) is already open-access available.
The paper is (quite a lot) extended version of the conference paper
Moreover, it also builds on results from the following journal papers: