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Modeling and Reasoning with Bayesian Networks
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The paper utilizes Port State Control inspection data for discovering interactions between the numbers of various types of deficiencies found on ships and between the deficiencies and ship's involvement in maritime traffic accidents and incidents. Bayesian network models for describing the dependencies of the inspection results, ship age, type, flag, accident involvement, and incidents reported by the Vessel Traffic Service are learned from the Finnish Port State Control data from 2009-2011, 2004-2010 Baltic Sea accident statistics and the reported Gulf of Finland Vessel Traffic Service incidents within 2004-2008. Two alternative Bayesian network algorithms are applied to the model construction. Further, additional models including a hidden variable which represents the complete system and its safety features and which links the accident and incident involvement and Port State Control findings are presented. Based on model-data fit comparisons and 10-fold cross-validation, a constraint-based learning algorithm NPC mainly outperforms the score-based algorithm repeated hill-climbing with random restarts. For the highest scoring models, mutual information and influence of evidence analyses are conducted in order to analyze which network variables and variable states are the most influential ones for determining the accident involvement. The analyses suggest that knowledge on ship type, the Port State Control inspection type and the number of structural conditions related deficiencies are among the ones providing the most information regarding accident involvement and the true state of the hidden system safety variable.