Learning Bayesian Networks from Correlated Data

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Learning Bayesian Networks from Correlated Data

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Title: Learning Bayesian Networks from Correlated Data
Author: Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

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Citation: Bae, Harold, Stefano Monti, Monty Montano, Martin H. Steinberg, Thomas T. Perls, and Paola Sebastiani. 2016. “Learning Bayesian Networks from Correlated Data.” Scientific Reports 6 (1): 25156. doi:10.1038/srep25156. http://dx.doi.org/10.1038/srep25156.
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Abstract: 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.
Published Version: doi:10.1038/srep25156
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857179/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:27320291
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