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- W323139643 abstract "AbstractWe address some computational issues that may hinder the use of AMP chain graphs in practice. Specifically, we show how a discrete probability distribution that satisfies all the independencies represented by an AMP chain graph factorizes according to it. We show how this factorization makes it possible to perform inference and parameter learning efficiently, by adapting existing algorithms for Markov and Bayesian networks. Finally, we turn our attention to another issue that may hinder the use of AMP CGs, namely the lack of an intuitive interpretation of their edges. We provide one such interpretation.KeywordsChain Graph (CG)Perform Parameter LearningIntuitive InterpretationBayesian NetworksIterative Proportional Fitting Procedure (IPFP)These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W323139643 created "2016-06-24" @default.
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- W323139643 date "2015-01-01" @default.
- W323139643 modified "2023-09-23" @default.
- W323139643 title "Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs" @default.
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- W323139643 doi "https://doi.org/10.1007/978-3-319-20807-7_30" @default.
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