A Bayesian Approach to Forensic Psychiatric Data

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dc.contributor.advisorGrootjen, F.A.
dc.contributor.advisorBorries, K. von
dc.contributor.advisorVuurpijl, L.G.
dc.contributor.authorMennes, L.
dc.date.issued2012-08-28
dc.description.abstractA dataset containing a large number of variables (4898) from Forensic Psychiatry is explored for this project. This dataset is provided by the forMINDs project by the Pompestichting. The method for exploration is generating a Bayesian network. The dataset has been strongly modified for this purpose. Variables have been discarded (1394 remaining), continuous variables are discretized and the large number of missing variables (30%) are imputed using distribution based imputation. For structure generation the PC-algorithm is used, with the -statistic for conditional independence testing. Computation time restrictions have resulted in a further reduction of the number of variables. The resulting network of 132 variables contained cycles, indicating the existence of hidden or selection variables and making the network unusable for parameter learning and inference. Secondly the network has an average of 19 neighbors per node, making it too complex for interpretation.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/108
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleA Bayesian Approach to Forensic Psychiatric Dataen_US
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