Bayesian classification based on multivariate binary data

Johnson, WO; Kokolakis, GE

dc.contributor.author	Johnson, WO	en
dc.contributor.author	Kokolakis, GE	en
dc.date.accessioned	2014-03-01T01:09:44Z
dc.date.available	2014-03-01T01:09:44Z
dc.date.issued	1994	en
dc.identifier.issn	0378-3758	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11168
dc.subject	Dirichlet distribution	en
dc.subject	entropy	en
dc.subject	kernel estimate	en
dc.subject	predictive density	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	CATEGORICAL-DATA	en
dc.subject.other	KERNEL METHOD	en
dc.subject.other	DISCRIMINATION	en
dc.title	Bayesian classification based on multivariate binary data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0378-3758(94)90152-X	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0378-3758(94)90152-X	en
heal.language	English	en
heal.publicationDate	1994	en
heal.abstract	Consider a disease which has associated with it d symptoms that are either present or absent. Several specific symptoms are known for an individual. The question is whether the person has the disease? This is a classification problem based on multivariate binary data. Our approach is Bayesian and involves the prediction of future d-vectors of binary responses. Underlying this problem is the implicit estimation of the corresponding 2d cell probabilities. This is difficult with low structure and with moderate or large d, unless the sample sizes for the training data are enormous. Our model incorporates a prior distribution on unknown parameters, and a 'smoothing' parameter that relates the cells to one another. The posterior is approximated in order to obtain cell probability estimates and an approximate predictive density. Consistency results are indicated, and the procedure is illustrated with data involving the diagnosis of a disease called 'dry eyes'. © 1994.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Journal of Statistical Planning and Inference	en
dc.identifier.doi	10.1016/0378-3758(94)90152-X	en
dc.identifier.isi	ISI:A1994PA44700002	en
dc.identifier.volume	41	en
dc.identifier.issue	1	en
dc.identifier.spage	21	en
dc.identifier.epage	35	en