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- W2003172213 abstract "In this paper, we propose a new method, named Greedy Equivalence Search-Expectation Maximization (GES-EM), for learning Bayesian networks from incomplete data. Our method extends the recently proposed Greedy Equivalence Search (GES) algorithm 10 to deal with incomplete data. For the quality evaluation of learned networks, we make use of the expected Bayesian Information Criterion (BIC) scoring function. In addition, we propose a new structural evaluation criterion. This so-called SEC criterion is more suitable than existing structural evaluation criteria, since it is based on the comparison of learned networks to the generating ones through Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show that GES-EM algorithm yields more accurate structures than the standard Alternating Model Selection-Expectation Maximization (AMS-EM) algorithm. 15 ." @default.
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- W2003172213 date "2008-03-01" @default.
- W2003172213 modified "2023-09-27" @default.
- W2003172213 title "LEARNING AND EVALUATING BAYESIAN NETWORK EQUIVALENCE CLASSES FROM INCOMPLETE DATA" @default.
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- W2003172213 doi "https://doi.org/10.1142/s0218001408006193" @default.
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