On open questions in the geometric approach to structural learning Bayesian nets

AbstractThe basic idea of an algebraic approach to learning Bayesian network (BN) structures is to represent every BN structure by a certain uniquely determined vector, called the standard imset. In a recent paper [18], it was shown that the set S of standard imsets is the set of vertices (=extreme points) of a certain polytope P and natural geometric neighborhood for standard imsets, and, consequently, for BN structures, was introduced.The new geometric view led to a series of open mathematical questions. In this paper, we try to answer some of them. First, we introduce a class of necessary linear constraints on standard imsets and formulate a conjecture that these constraints characterize the polytope P. The conjecture has been confirmed in the case of (at most) 4 variables. Second, we confirm a former hypothesis by Raymond Hemmecke that the only lattice points (=vectors having integers as components) within P are standard imsets. Third, we give a partial analysis of the geometric neighborhood in the case of 4 variables