A Bayesian Network for Predicting Body Composition of Human Body

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dc.contributor.authorOloyede, Isiaka
dc.date.accessioned2019-10-15T10:56:36Z
dc.date.available2019-10-15T10:56:36Z
dc.date.issued2016
dc.descriptionA probabilistic graphical model is known as belief network or Bayesian network which is a joint probability distribution over a set of variables and the corresponding local univariate distributions. It actually comes from the recursive use of Bayes theorem to decompose the joint distribution to individual distribution of the nodes given the direct acyclic graph.en_US
dc.description.abstractThis study investigates the predictive body composition adopting age, height, weight, waist circumference and body mass index as the covariates to predict three components that form the body weight: bone, fat and lean. Both saturated Bayesian network and convenient Bayesian network were compared using absolute bias, standard deviation and root mean square error to ascertain consistency and efficiency of the two types of Bayesian networks. The Directed Acyclic Graph were used to compare the two networks; nine different samples sizes were constructed using uniform distribution for both dependent variables and covariates. The study observed that saturated Bayesian network outperformed convenient Bayesian network both at small and medium sample sizes whereas the two networks estimators converged at higher sample sizes yielding the same standard deviation and root mean squares error. The study therefore recommends that saturated Bayesian network should be used at small sample sizes while any of the two types could be used at higher sample sizes.en_US
dc.description.sponsorshipselfen_US
dc.identifier.citationOloyede I.(2016); A Bayesian Network for Predicting Body Composition of Human Body, Centrepoint Journal (Science Edition), Volume 22, No.1, pages 101-113,http//www.unilorin.edu.ng/centrepointen_US
dc.identifier.urihttp://hdl.handle.net/123456789/2542
dc.language.isoenen_US
dc.publisherCentrepoint Journal (Science Edition), university of ilorinen_US
dc.relation.ispartofseries;22
dc.subjectDAGsen_US
dc.subjectSaturated Bayesian Networken_US
dc.subjectConvenient Bayesian Networken_US
dc.titleA Bayesian Network for Predicting Body Composition of Human Bodyen_US
dc.typeArticleen_US

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