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10.1038/srep25156 http://scihub22266oqcxt.onion/10.1038/srep25156 C4857179!4857179!27146517     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27146517.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2016 ; 6 (ä): ä Nephropedia Template TP {{tp|p=27146517|t=2016. Learning Bayesian Networks from Correlated Data |pdf=|usr=}}{{27146517}} gab.com Text Learning Bayesian Networks from Correlated Data JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27146517 #FOAvip 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. Twit Text FOAVip Learning Bayesian Networks from Correlated Data ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27146517 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27146517 #FOAvip English Wikipedia <ref>{{Cite pmid|27146517}}</ref> Learning Bayesian Networks from Correlated Data #MMPMID27146517 Bae H; Monti S; Montano M; Steinberg MH; Perls TT; Sebastiani P Sci Rep 2016[]; 6 (ä): ä PMID27146517show ga 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. äLearning Bayesian Networks from Correlated Data (epmc).pdf DeepDyve Pubget Overpricing