2012/10/16 by Daniel Lowd, Lowd, Daniel · 2 citations
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1210.4896
Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)
arxiv created 2012/10/16 · arxiv updated 2012/10/19
Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of evaluating candidates structures. Dependency networks (DNs) represent a probability distribution as a set of conditional probability distributions. DNs are very fast to learn, but the conditional distributions may be inconsistent with each other and few inference algorithms support DNs. In this paper, we present a closed-form method for converting a DN into an MN, allowing us to enjoy both the efficiency of DN learning and the convenience of the MN representation. When the DN is consistent, this conversion is exact. For inconsistent DNs, we present averaging methods that significantly improve the approximation. In experiments on 12 standard datasets, our methods are orders of magnitude faster than and often more accurate than combining conditional distributions using weight learning.