Statistics – Machine Learning
Scientific paper
2011-01-10
Statistics
Machine Learning
Changes from v3 to v4: Section 1 has been extended with more motivation and a review of the literature. Theorem 3 proves that
Scientific paper
Suppose that multiple experts (or learning algorithms) provide us with alternative Bayesian network (BN) structures over a domain, and that we are interested in combining them into a single consensus BN structure. Specifically, we are interested in that the consensus BN structure only represents independences all the given BN structures agree upon and that it has as few parameters associated as possible. In this paper, we prove that there may exist several non-equivalent consensus BN structures and that finding one of them is NP-hard. Thus, we decide to resort to heuristics to find an approximated consensus BN structure. In this paper, we consider the heuristic proposed in \citep{MatzkevichandAbramson1992,MatzkevichandAbramson1993a,MatzkevichandAbramson1993b}. This heuristic builds upon two algorithms, called Methods A and B, for efficiently deriving the minimal directed independence map of a BN structure relative to a given node ordering. Methods A and B are claimed to be correct although no proof is provided (a proof is just sketched). In this paper, we show that Methods A and B are not correct and propose a correction of them.
No associations
LandOfFree
Finding Consensus Bayesian Network Structures does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Finding Consensus Bayesian Network Structures, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Finding Consensus Bayesian Network Structures will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-455537