The construction of a Bayesian Network (BN) model entails two major tasks: realization of the model structure, and the calibration (parameterization) of the model. BN model constructors, ab initio, relied only on domain experts to define both the structure and parameters of a model.
Currently, algorithms exist to construct BN models from data. Consequently, there are three BN model construction techniques: total expert-centred, total data-centred, and semi data-centred. We empirically investigated which of these approaches is the optimal approach for the construction of a BN model for our intended application. The investigation yielded some interesting themes.
|Title of host publication||Proceedings of the World Congress on Engineering 2012. Vol 2|
|Place of Publication||Hong Kong|
|Number of pages||2065|
|Publication status||Published - 4 Jul 2012|
|Event||World Congress on Engineering - London|
Duration: 4 Jul 2012 → 6 Jul 2012
|Name||Lecture notes in engineering and computer science|
|Conference||World Congress on Engineering|
|Period||4/07/12 → 6/07/12|