2013/02/13 by Yang Xiang, Xiang, Yang, Michael S. K. M. Wong +3 · 39 citations
Computer Science · Mathematics · #AI-based Problem Solving and Planning #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Class (philosophy) #Computer science #Conditional independence #Dependency (UML) #Domain (mathematical analysis) #FOS: Computer and information sciences #Heuristics #Independence (probability theory) #Inference #Link (geometry) #Machine Learning and Algorithms #Machine learning #Mathematics #Probabilistic logic #Search algorithm #Theoretical computer science #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.3612
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)
arxiv created 2013/02/13 · openalex publication_date 2013/02/13 · arxiv updated 2013/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
In learning belief networks, the single link lookahead search is widely adopted to reduce the search space. We show that there exists a class of probabilistic domain models which displays a special pattern of dependency. We analyze the behavior of several learning algorithms using different scoring metrics such as the entropy, conditional independence, minimal description length and Bayesian metrics. We demonstrate that single link lookahead search procedures (employed in these algorithms) cannot learn these models correctly. Thus, when the underlying domain model actually belongs to this class, the use of a single link search procedure will result in learning of an incorrect model. This may lead to inference errors when the model is used. Our analysis suggests that if the prior knowledge about a domain does not rule out the possible existence of these models, a multi-link lookahead search or other heuristics should be used for the learning process.