2013/02/06 by Jun Hu, Jian Hu, Yang Xiang +2 · 11 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Graph Neural Networks #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Bounded function #Computer science #Conditional independence #Domain (mathematical analysis) #FOS: Computer and information sciences #Independence (probability theory) #Machine Learning (cs.LG) #Mathematics #Multi-Criteria Decision Making #Probabilistic logic #Set (abstract data type) #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1302.1549
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)
arxiv created 2013/02/06 · openalex publication_date 2013/02/06 · arxiv updated 2013/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A pseudo independent (PI) model is a probabilistic domain model (PDM) where proper subsets of a set of collectively dependent variables display marginal independence. PI models cannot be learned correctly by many algorithms that rely on a single link search. Earlier work on learning PI models has suggested a straightforward multi-link search algorithm. However, when a domain contains recursively embedded PI submodels, it may escape the detection of such an algorithm. In this paper, we propose an improved algorithm that ensures the learning of all embedded PI submodels whose sizes are upper bounded by a predetermined parameter. We show that this improved learning capability only increases the complexity slightly beyond that of the previous algorithm. The performance of the new algorithm is demonstrated through experiment.