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Learning Bayesian Networks from Correlated Data Public Deposited

https://ir.library.oregonstate.edu/concern/articles/v979v4733

This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Nature Publishing Group. The published article can be found at:  http://www.nature.com/articles/srep25156

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  • Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.
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  • Bae, H., Monti, S., Montano, M., Steinberg, M. H., Perls, T. T., & Sebastiani, P. (2016). Learning Bayesian Networks from Correlated Data. Scientific Reports, 6, 25156. doi:10.1038/srep25156
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  • This work was funded by the National Institute on Aging (NIA U19-AG023122, U01-AG023755 to T.P.), the National Heart Lung Blood Institute (R21HL114237 to P.S.), and the National Institure of General Medical Sciences T32GM074905.
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  • description.provenance : Made available in DSpace on 2016-06-15T14:41:39Z (GMT). No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) BaeLearningBayesianNetworks.pdf: 1777547 bytes, checksum: 1700b69128a1b9b37ff43bb145e5316a (MD5) BaeLearningBayesianNetworksSupplementaryInformation.pdf: 80049 bytes, checksum: e7232bc2bbc068067162727b4b10e54c (MD5) Previous issue date: 2016-05-05
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2016-06-15T14:41:39Z (GMT) No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) BaeLearningBayesianNetworks.pdf: 1777547 bytes, checksum: 1700b69128a1b9b37ff43bb145e5316a (MD5) BaeLearningBayesianNetworksSupplementaryInformation.pdf: 80049 bytes, checksum: e7232bc2bbc068067162727b4b10e54c (MD5)
  • description.provenance : Submitted by Patricia Black (patricia.black@oregonstate.edu) on 2016-06-15T14:41:14Z No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) BaeLearningBayesianNetworks.pdf: 1777547 bytes, checksum: 1700b69128a1b9b37ff43bb145e5316a (MD5) BaeLearningBayesianNetworksSupplementaryInformation.pdf: 80049 bytes, checksum: e7232bc2bbc068067162727b4b10e54c (MD5)

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