A Bayesian Approach to Forensic Psychiatric Data
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2012-08-28
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en
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Abstract
A 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.
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Faculteit der Sociale Wetenschappen