## 6 Conclusions  
In this work, a complete methodology for assessing the risk factors of a maritime accident was developed that joins and exploits two main features of the Bayesian belief network framework: (i) probabilistic semantics of a Bayesian belief network that allows for modelling with uncertainty and incomplete data, and (ii) bi-directional information flow from \"cause-to-effect\" and \"effect-to-cause\". In summary, a twofold contribution can be distilled. First, the complete framework for modeling and risk factor assessment of maritime accidents is systematized. Second, the novel measure for quantitative identification of contributing causal factors, named the Lyapunov-based divergence measure, is proposed that enables validation through diagnostic inference in BBN and identification of multiple causation factors. The advantage of the application of the Lyapunov-based divergence measure is twofold: it enables model validation through efficient use of existing real world accident data, and it allows a diagnostic of future cases and scenarios. The developed model and subsequent analysis expand the understanding of the influential factors on the maritime accident. The complete methodology ultimately supports decision-making when adopting certain measures to increase the maritime safety. Though the complete framework was showcased on a grounding of small passenger ships in the Adriatic, it can easily be generalized to risk assessment of other major maritime accident types. The limitations of the current work are related to the uncertainty of the network structure and node parametrization, due to the influence of expert knowledge, which might not be as objective as data-driven structuring and parametrization would be. This limitation, however, is not unique to our approach, yet it is inherent to the expert elicited BN. An overview of limitations of BNs in the maritime accident domain is well covered in the work of [43]. In the future work, the root cause identification analysis with the proposed Lyapunov-based divergence measure will be used to assess different accident-reducing actions through conjunctive query inferences and thus will enable the development of a system for case-action assessment. This will require the deployment of a simulation environment with a scenario-based data generation process. In order to achieve this goal, the developed static BN will be transferred into a dynamic Bayesian network framework, which will enable improved state-space analysis and consequent risk factor identification. This adaptation will give rise to interesting new challenges, and one of a few problems that will have to be investigated is how to perform a validation of the state-space varying Bayesian network as it will aggravate the real world data scarcity problem.  
Conceptualization, A.K.S.; methodology, L.V. and A.K.S.; software, L.V. and A.K.S.; validation, A.K.S.; formal analysis, L.V.; investigation, L.V.; writing--original draft preparation, L.V. and A.K.S.; writing--review and editing, L.V. and A.K.S.; supervision, D.M. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.