Linked Life Data

9821517

Source:http://linkedlifedata.com/resource/pubmed/id/9821517

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1999-2-18
pubmed:abstractText
In this paper Bayesian networks modelling is applied to a multidimensional model of depression. The characterization of the probabilistic model exploits expert knowledge to associate latent concentrations of neurotransmitters and symptoms. An evolution perspective is also considered. Specific criteria are introduced to detect the influence of the latent variable on the observation of symptoms. The Bayesian analysis is carried out using Gibbs sampling technique which is implemented in the BUGS software. The estimation phase leads to the selection of symptoms entering into the definition of behavioral syndromes. Results on real data are discussed. The last section deals with simulation experiments. Simulation results confirm our methodological choices. Results of the paper can enlarge to the central problem of the management of latent variables in Bayesian networks modelling.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0933-3657
pubmed:author
pubmed:issnType
Print
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
259-77
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Modelling behavioral syndromes using Bayesian networks.
pubmed:affiliation
Département de Biomathématiques et Service d'Informatique Médicale, C.H.U. Pitié-Salp?trière, Paris, France.
pubmed:publicationType
Journal Article